Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations1Modelling Greenhouse Gas Fluxes from Chinas Agriculture,Forestry and Land Use Sector:Gaps and recommendationsMay 2023Acknowledgements Executive Summary1.Introduction2.Chinas agricultural development and GHG fluxes3.Methods and data4.How do the models compare in their coverage?4.1.System boundary and coverage of land use categories4.2.Coverage of LULUCF pools4.3.Coverage of LULUCF in models versus the National GHG Inventory4.4.Coverage of agricultural non-CO2 emissions versus the National GHG Inventory5.How do the model projections compare?5.1.Projections for the LULUCF sector5.2.Projections for the agriculture sector6.Knowledge gaps and recommendations to improve modelsAnnex I:Model descriptionsAnnex II:Data and scenario interpretation for Figure 4References3458 1214 15 1820 20 22232628 3235 36ContentsAuthorsJinfeng ChangCollege of Environmental and Resource Sciences,Zhejiang University,310058 Hangzhou,China;Email:Sha YuCenter for Global Sustainability,University of Maryland,College Park,Maryland 20742,United States of America;Email:shaumd.eduYanrong CuiSchool of Agricultural Economics and Rural Development,Renmin University of China,100872,Beijing,P.R.China;Email:cuiyr_Minpeng ChenRenmin University of China,100872,Beijing,P.R.China;Email:Nicklas ForsellInternational Institute for Applied Systems Analysis(IIASA),A-2361 Laxenburg,Austria;Email:forselliiasa.ac.atAline MosnierSustainable Development Solutions Network(SDSN),18 rue Bergre,75009 Paris,France;Email:aline.mosnierunsdsn.orgXiaoxi WangChina Academy for Rural Development,Department of Agricultural Economics and Management,Zhejiang University,Hangzhou,China;Email:xiaoxi_ FOLU is grateful to Norways International Climate and Forest Initiative(NICFI),which funded this publication.The authors acknowledge support from Zhaohai Bai,Wei Li,Junling Liu,Zhu Liu,Xiaoxi Wang,and Feng Zhou.The views expressed here are those of the authors and do not necessarily reflect the views of the associated and/or supporting institutions.The authors acknowledge the suggestions from all peer reviewers,particularly Fei Teng and Martjan Schelhaas,and various colleagues,which have helped shape the final version.Thanks also to Seth Cook,Olaf Erenstein and Talia Smith for reviewing the publication,to Fiona Hinchcliffe for excellent editing,and to Regency Creatives for superb design work.The usual disclaimer applies.Suggested citation:Minpeng Chen,Nicklas Forsell,Sha Yu,Jinfeng Chang,Xiaoxi Wang,Aline Mosnier,Yanrong Cui,2023.Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations.The Food and Land Use Coalition,London.AcknowledgementsModelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations4Executive summaryThis assessment evaluates existing greenhouse gas(GHG)projections for the Chinese agriculture,forestry and land use(AFOLU)sector and the models being used to create them.A total of eight models(GLOBIOM-China,MAgPIE-China,GCAM5.3,FABLE Calculator-China,ORCHIDEE,PECE-LIU,AGHG-INV and SRNM)are compared and analysed for their coverage of sectors,carbon pools,land use categories,and projections.In terms of the land use,land use change,and forestry(LULUCF)sector,the models variously cover the most relevant land use categories:forest land,grassland,and cropland.The Intergovernmental Panel on Climate Change(IPCC)tier methods used and the coverage of specific carbon pools differ widely between the models.In the agricultural sector,the eight models have some similarities in their structure and scope,with Tier 2 dominating for the calculation of GHG fluxes.There are significant uncertainties in existing model estimates regarding historical and projected future LULUCF emissions and removals for China.The difference in historical net emission estimates between the models is striking,at 1,119 million tons of carbon dioxide equivalent(MtCO2eq),which corresponds to the estimated size of Chinas national GHG sink in 2014(1,150 MtCO2eq).In terms of future developments of the LULUCF sector,the models cannot provide a unified picture as to whether Chinas current national GHG sink will increase or decrease.For the agricultural sector,most models project a business-as-usual development in which the non-CO2 emissions(mainly CH4 and N2O)from Chinas agricultural sector increase at a much more moderate rate after 2030,and peak between 2045 and 2060.The peak emission projections range from 800 to 1,400 MtCO2eq.The future mitigation potential estimates for agriculture range from 200 to 800 MtCO2eq.The ranges reflect differences in base years,model structure,scenario assumptions,and parameter selection.As a whole,the models differ considerably in their representations of the Chinese AFOLU sector.As a result,caution is needed when comparing projections and when using them to formulate policy targets.The assessment thus highlights the need for prioritized actions and further development of the Chinese AFOLU sectorial models so that they can assist in setting targets and developing reliable and clear pathways for the AFOLU sector in line with limiting global warming to 1.5C or well below 2C.Priority actions include more collaboration in model development,better data sharing and access,and promoting multi-model comparison.1.IntroductionModelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations5Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations6The Paris Agreement sets a long-term target of keeping the increase in global average temperature to well below 2C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5C.To achieve the temperature goals,countries will need to plan for a profound and rapid transformation of all sectors.Forestry and agriculture activities are integral to any mitigation strategy for greenhouse gas(GHG)emissions and removals.Land management and land use change activities in these sectors can act as a source of GHG emissions including carbon dioxide(CO2),methane(CH4)and nitrous oxide(N2O)or as a sink.Carbon sinks are defined as carbon pools,other than the atmosphere,where CO2 is removed from the atmosphere by sequestration.Globally the agriculture,forestry,and other land use(AFOLU)i sector is responsible for the gross emissions of approximately 11.2 gigatons of carbon dioxide equivalent per year(GtCO2eq per year).2,3,4 This is about 25%of all net anthropogenic GHG emissions,with nearly half of the amount coming from agriculture and half from land use change.5,6,7 At the same time,the economic mitigation potential within the land use sector is substantial:removals from the land use,land use change,and forestry(LULUCF)ii sector are estimated at between 1.92.1 and 3.13.3 GtCO2eq per year at a carbon price of USD 20 and USD 100 per t CO2eq by 2030,respectively.8 Furthermore,around 1 GtCO2eq per year of emissions from the agriculture sector could be mitigated by 2030 at a carbon price of USD 25 per tCO2eq.While there is significant mitigation potential within the land use sector,the demands on the sector are also increasing for the provision of food,biomass,biodiversity,and tourism creating challenges for land use planning.A recent systematic literature review and meta-analysis suggest that global food demand will increase by 35 to 56tween 2010 and 2050,driven by population increase and changing diets.On a global level,total GHG emissions from agriculture continue to grow at approximately 1%per year,and agriculture is expected to remain one of the most significant contributors to world emissions in 2030.9,10,11 Under a 1.5C scenario,the global AFOLU sector could feasibly and sustainably contribute about 30%,or 15 GtCO2eq per year,of the global mitigation needed in 2050(including bioenergy with carbon capture and storage,or BECCS).This will require a transformation of the AFOLU sector worldwide,both production and consumption aspects.12 On September 22nd 2020,at the general debate of the 75th session of the United Nations General Assembly,China announced that it was aiming for carbon neutrality before 2060,demonstrating its determination to pursue a new model of economic growth and development.The transformation will be profound and comprehensive,but benefits and impacts will be unevenly distributed across sectors and regions.With almost 18%of the worlds population,food security is a top priority in Chinas national socio-economic development strategies and plans.With the population expected to peak at 1.45 billion in 2024,and diets shifting towards more animal-based protein,most business-as-usual(BAU)scenarios project non-CO2 GHG emissions from the agriculture sector to increase,making it more challenging for the country to decarbonise.13 From 1994 to 2014,non-CO2 GHG emissions from Chinas agricultural sector increased by about 37%,while the LULUCF sector was a net carbon sink.14 To achieve a 1.5C-consistent pathway for China,GHG emissions from the agricultural sector alone will need to peak by 2030,and the LULUCF sector must continue its role of net carbon sink.15 However,unlike other sectors(including energy,transportation,and industry),there is limited knowledge surrounding feasible and sustainable mitigation strategies,goals,and roadmaps for reducing non-CO2 GHG emissions and i.The agriculture,forestry and other land use(AFOLU)term encompasses agriculture as well as the land use,land use change and forestry(LULUCF)category.ii.Following the 2006 IPCC guidelines,the LULCUF sector includes reporting of fluxes related to changes within and conversions between all land use types including:forest land,cropland(including the soil carbon pool),grassland,wetlands,settlements and other land.The sector also includes reporting of fluxes related to changes to the harvested wood products pool.enhancing carbon sinks in the AFOLU sector in China.Building this knowledge will require a systematic,comprehensive,and in-depth analysis of long-term emission trajectories,their temporal and spatial characteristics,possible technology options,technical and economic mitigation potentials,and the economic impacts of mitigation technologies or practices in the AFOLU sector.In this context,this study conducted a systematic analysis of existing economic and biophysical models that simulate future potential GHG emissions and removals,land cover change,and commodity production in the Chinese AFOLU sectors.The assessment analysed and identified similarities and critical differences between the models themselves and their existing projections of GHG emissions and removals for China.We explored the key features of the different modelling tools and assessed how the underlying differences in modelling characteristics influence the projections,including critical assumptions,regional and sectoral coverage,coverage of carbon pools and gases,and the inclusion of mitigation technologies and mitigation options.Based on this analysis,we evaluate the uncertainty ranges of future emissions and removals for the Chinese AFOLU sector.We conclude with some recommendations for closing the knowledge gaps to improve future model development.Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations72.Chinas agricultural development and GHG fluxesModelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations8Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations9China has a long farming history with a tradition of intensive cultivation,and a substantial rural population.Agriculture has long been the bedrock of Chinas political and economic stability,and the Chinese government has placed a high priority on its development.Since 1978,China has succeeded in producing one-fourth of the worlds grain and feeding one-fifth of the worlds population from less than 10%of the worlds arable land,16,17 which is a major achievement for food security in China and the world.Currently,China ranks first in the world for the production of cereals,cotton,fruit,vegetables,meat,poultry,eggs,and fishery products,and has built up agricultural exchange and co-operation relations with more than 140 countries worldwide.18Figure 1 gives an historical overview of Chinas agricultural economy.The agriculture sector has grown steadily since 1990.Growth slowed after 1997,but took off again from 2003.Since 2008,agriculture has contributed to total factor productivity(TFP)growth as much as Chinas non-agricultural sectors.19,20 From 2000 to 2020,domestic grain production and the total output value of agriculture increased by 45%and 139%respectively(in 1980 constant prices),with intensification and growth of high-value agricultural products(vegetables,fruit,meat,milk,etc.)playing a critical role.21 Despite the challenges posed by COVID-19,plant pests,and natural disasters,China saw its 17th consecutive bumper year in 2020,with grain output up 0.9%year-on-year to nearly 670 billion kg in 2020.It marked the ninth consecutive year the countrys total grain production exceeded 600 billion kg,a production of a good harvest.The annual policy blueprint,known as the“No.1 document”,placed greater emphasis on food security than in 2021,calling for all provinces to improve grain yields during the 14th five-year period(2021-2025)as the pandemic hit top food-exporting nations and raised concerns about the stability of food supplies.Figure 1:Historical trends in Chinas agricultural sector18000160001400012000100008000600040002000016001400120010008006004002000Gross value of agriculture(trillion RMB)Grain production(million tons)Population(million)Gross value of agriculture(trillion RMB,1980 constant price)Population(million)Grain production(million tons)Data Source:National Bureau of Statistics,202222Along with the increase in agricultural productivity and income levels,the dietary structure of the population has changed dramatically,with a steady increase in the consumption of animal foods such as red meat,poultry meat,dairy and eggs,which poses a major health and environmental challenge for China.23 In 2019,per capita staple food consumption(i.e.rice,maize,wheat,and other grains)was 145.8kg,down 33.5%from 1997,24 while per capita fruit and meat consumption has grown significantly,reaching 58.6kg and 60.3kg in 2019,respectively,an increase of 90%and 190%.25 The sector will face greater challenges to decarbonize because of an expected rise in demand for animal-based products.26Due to industrialization and urbanization in China,labour,land and other important agricultural inputs have become increasingly expensive.Combined with the small scale of agriculture and the changing structure of rural populations,this has resulted in higher production costs for Chinese bulk agricultural products than for major exporters like the United States,Canada,and Australia.27 Rice,wheat,and corn prices reached US$398.6/ton,US$330.9/ton,and US$334.9/ton,respectively,in 2020,19.1%,78.3%,and 102.2%higher than international prices.As a result of price differences,agricultural imports have increased beyond the normal demand and production gap,leading to the importation of non-essential food.28 From 2001 to 2020,Chinas imports of broad grains,including soybeans,increased by about 7.2 times,from 17.38 million tons to 142.55 million tons.A market-driven increase in non-essential agricultural imports does not necessarily reduce domestic environmental pressures on agricultural development,but it negatively impacts Chinas agricultural industry and threatens the employment and income of farmers.29Chinas agricultural success has come at a price.Its agricultural emissions grew by 37.2%,from 605 MtCO2eq per yr in 1994 to 830 MtCO2eq in 2014 and now account for 13%of global agriculture-related GHG emissions.30,31 However,the contribution of the agriculture sector to total national emissions has declined,from 18.7%to 7.5%,as total emissions across sectors in China have increased even more.Whereas agriculture emits GHGs,the LULUCF sector presents a net sink in China(Figure 2).Rapid afforestation has seen the LULUCF sinks increase from sequestering 407 MtCO2eq per year in 1994 to 1,115 MtCO2eq in 2014(the most recent year for which official data is available).Among the emissions from agricultural sources,enteric fermentation from ruminants was the largest source of methane(CH4)emissions and agricultural soils were the largest source of nitrous oxide(N2O)emissions.In 2014,GHG emissions from enteric fermentation,manure management,rice cultivation,agricultural soils,and field burning of agricultural residues were 207 MtCO2eq,139 MtCO2eq,187 MtCO2eq,288 MtCO2eq and 9 MtCO2eq respectively,accounting for 24.9%,16.7%,22.5%,34.7%and 1.1%of total emissions from agricultural sources.Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations10Figure 2:Composition of AFOLU GHG emissions and removals for China in 2010 and 2014-400-600-800-1000-1200Million metric ton CO2-eq4002000-200100080060020102014Forest landHarvested wood productsCroplandGrasslandWetlandSettlementsAgricultural soilsEnteric fermentationManure managementRice cultivationField burningNet agricultureNet LULUCFNet AFOLUData source:Government of China,201832;Government of China,201633;Government of China,2018.34Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations113.Methods and dataModelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations12Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations13To ensure a systematic and uniform review of existing GHG projections for the Chinese AFOLU sector and the models being used to develop them,researchers from the International Institute for Applied Systems Analysis(IIASA)and Renmin University of China developed two standardized reporting templates.These reporting templates were developed such that the key features of Chinese models and GHG projections could be collected in a standardized manner.The“modelling features template”focused mainly on the critical features of the models themselves.It aimed to improve our understanding of how differences in these features influence projections.The template included aspects such as the coverage of pools and gases,representation of land use categories,accountability of land use conversions,and regional and sectoral coverage of the model.The“projections template”aimed to improve our understanding of existing projections.The template allowed the project partners to report on projections from 2010 to 2100 for crucial variables such as land use transitions,developments of pools and gases,food and feed production,and other indicators such as food security(where included in the model).To facilitate smooth and correct inclusion of data in the reporting templates and thereby ensure high-quality analysis of the critical features of the models,both included extensive instructions and pre-filled examples.Once developed,the two templates were circulated to the project partners to validate and clarify any uncertainties.All project partners assisted with this work and provided valuable insights and ideas to improve the templates.Once the reporting templates were finalised and tested,we reached out to the 10 key modelling teams working within and outside China for their input,either focusing on a specific AFOLU sub-sector,a full AFOLU sector or on AFOLU as part of integrated assessment models.Ten key modelling teams were selected based on a mapping of the existing modelling landscape in China,with a focus on those with documented projections for the Chinese AFOLU sector.However,this report only includes models that provide national coverage for China and projections of GHG emissions and removals to 2050/2060(reporting years varied across the models).This comparison therefore assesses a total of eight models:GLOBIOM-China,MAgPIE-China,GCAM5.3,FABLE Calculator-China,ORCHIDEE,PECE-LIU,AGHG-INV and SRNM.Other potential candidates,such as DNDC,APSIM,and CENTURY/DayCent,have not been included in this assessment as no projections could be provided in time for this study.The reporting templates were circulated to all modelling teams by September 2021.After that,we interacted directly with each modelling team to provide clarifications and further explanations on how to reflect the specifics of their model assumptions in the two reporting templates.All eight modelling teams filled in and reported on the modelling feature template,and five modelling teams submitted the projection template.It should be noted that the modelling teams submitted their individual latest projections for this assessment,thus no harmonization has been performed between the modelling teams in terms of underlying data sources(e.g.,emission coefficients,land use,mitigation options)or key drivers for their projections(e.g.,social economic development,climate change,international trade,national/international policies).Further information about the individual models can be found in Annex I.4.How do the models compare in their coverage?Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations14Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations154.1.System boundaries and coverage of land use categoriesBased on the information received from the modelling teams,we made a preliminary assessment of the current state of modelling capacities for China(Table 1).In this section we first discuss the coverage of the models that include the LULUCF sector(GLOBIOM-China,MAgPIE-China,GCAM5.3,FABLE Calculator-China,ORCHIDEE and PECE-LIU),and then focus on the models that cover the agriculture sector(GLOBIOM-China,MAgPIE-China,GCAM5.3,FABLE Calculator-China,AGHG-INV and SRNM).Six models cover all or parts of the LULUCF sector,with a focus on forest land,cropland,grassland and other land(Table 2).iii According to the Chinese National GHG Inventory of 2014,35 these four land use types account for 90%(675.29 Mha)of Chinas total land area.All these models account for developments for forest land,with only one model(PECE-LIU)not accounting for cropland or grassland.As many as four models(GLOBIOM-China,MAgPIE-China,GCAM5.3 and FABLE Calculator-China)also account for developments on other land(73.63 Mha).However,a low level of attention has been paid to wetlands(39.73 Mha)and settlements(37.23 Mha).Of the models reviewed,only MAgPIE-China includes wetlands,and only GCAM5.3 and the FABLE Calculator-China models account for settlements.iv Of the six models that include forest land,three distinguish between managed and unmanaged forest land.Of the five models that include grassland,two distinguish between managed and unmanaged grassland.MAgPIE-China,the only model that considers wetlands,also distinguishes between managed and unmanaged wetlands.No information could be retrieved from the Chinese National GHG Inventory of 2014 on the split between managed and unmanaged land for the different land use categories.Thus,the existing models vary in their scope and their representation of key features of Chinas major land uses and agriculture sector.In addition,none of the models that cover the LULUCF sector cover all land use types.iii.According to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories,other land includes bare soil,rock,ice,and all land areas that do not fall into any of the other five main land use categories,i.e.,forest land,cropland,agriculture,grassland,and settlements.iv.Settlements are treated as static in MAgPIE-China.Table 1.Comparison of main model featuresModel nameAFOLU sectoral coverageMethodologyGeographical coveragePurposeFirst year of projectionsTime horizon and time stepsGLOBIUM-ChinaMAgPIE-ChinaGCAM5.3FABLE Calculator-ChinaORCHIDEEPECE-LIUAGHG-INVSRNMAgriculture&LULUCFAgriculture&LULUCFAgriculture&LULUCFAgriculture&LUC*LULUCFForest sectorAgricultureCropping systemPartial equilibrium modelEquilibrium model without optimisationProcess-based modelOptimization modelInventoryStatistical modelMulti-regionMulti-regionMulti-regionNationalMulti-regionNationalNationalNationalForecastingForecastingForecastingForecasting,policy simulationForecasting,back-casting,policy simulationForecastingForecastingBack-casting20102010201520002015*201020182017Long-term,every 10 years until 2100Long-term,every 5 years until 2100Long-term,every 5 years until 2100Long-term,every 5 years until 2050Long-term,every year until 2100/2300Long-term,every 5 years until 2060Long-term,every year until 2050Long-termPartial equilibrium modelPartial equilibrium model*The FABLE Calculator-China model accounts for land-use change(LUC)but not does not account for GHG fluxes related to existing land use or forest management.*For the ORCHIDEE model this varies between scenarios;however,for CMIP6 the future climate starts from 2015.Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations16Table 2.Comparison of models land-use coverage and land area estimates for China(2014)Data/Model2014 land area estimates(Mha)36GLOBIOM-ChinaMAgPIE-China GCAM5.3FABLE Calcu-lator-China*ORCHIDEEForest land total(i.e.,managed unmanaged)Forest land(managed)Forest land(unmanaged)CroplandGrassland total(i.e.,managed unmanaged)Grassland(managed)Grassland(unmanaged)Wetlands total(i.e.,managed unmanaged)180.04*135.06286.5639.73PECE-LIUWetlands(managed)Wetlands(unmanaged)Settlements37.23Other land73.63*In the FABLE Calculator-China,emissions from grassland conversion to cropland and emissions/sequestration from cropland conversion to grassland have not been included so far.However,carbon sequestration occurring on abandoned agricultural land is accounted for.*Based on the information provided in the 2014 National GHG Inventory,forest land has been calculated as the sum of high,bamboo and open forests.Other land has been calculated as shrub land.Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations17Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations184.2.Coverage of LULUCF poolsAs the coverage of greenhouse gases and the methods used to estimate the size of emissions and removals vary between models,it is important to identify the coverage of each of the different land use models.For the six models that cover all or parts of the LULUCF sector,we assessed their coverage of carbon pools for the different land use categories,as well as the IPCC tier methodsv that were used to calculate the development for each pool(Table 3).We find that the carbon pools associated with forest land(including forest land,remaining forest land and land converted to forest land)have received a high level of attention by the different modelling teams(Table 3).In contrast,to date,far less attention has been paid to carbon pools associated with wetlands,settlements,other land,and harvested wood products(HWP).In terms of soil carbon pools,the models have paid more attention to representing mineral soils than organic soils.Of the six models that cover the land use sector,only two(ORCHIDEE and GCAM5.3)report that they account for the emissions by source and removals by sinks for CH4 and N2O for the LULUCF sector(Table 3).Large variation can be noted in the IPCC tier methods used by the different models to calculate the development of specific carbon pools(Table 3).However,there are some patterns in the methods being applied.For example,Tier 3 methods are predominantly used for calculating carbon pools related to forest land.For carbon stock changes in living biomass,all but one model applies Tier 3 methods.The only exception is the FABLE Calculator-China,which uses a Tier 2 method for carbon pools on afforested land and abandoned agricultural land(changes in living biomass in managed forests are not accounted for).In terms of carbon stock changes in deadwood,litter,and dead organic matter,the four models that represent these pools use Tier 3 methods.For carbon pools in wetlands and settlements,Tier 1 and Tier 2 methods are predominantly used.Tier 1 and Tier 2 methods are also predominantly used for soil carbon pools.The only exception is the ORCHIDEE model,which applies a Tier 3 method for soil carbon pools in forest land,cropland and grassland.v.Methods used to estimate GHG fluxes are usually categorized into three tiers based on the IPCC methodology(2006,2019),with a tier generally representing a level of methodological complexity.Tier 1 is the basic method,frequently using IPCC-recommended default values,while Tier 2 applies country-specific emission factors.Tier 3 estimates are more demanding in terms of complexity and data requirements.Tiers 2 and 3 are sometimes referred to as higher tier methods and are generally considered to be more accurate,as long as adequate data are available to develop,evaluate and apply them.Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations19GCFGCGCGCMMMTable 3.Models coverage of carbon pools and use of tier methods(T1,T2 or T3)to calculate changesLand use represented in the modeCarbon stock change in living biomass per areaGainsForest land Forest land remaining forest landLand converted to forest landCropland Cropland remaining croplandLand converted to croplandGrasslandGrassland remaining grasslandLand converted to grasslandWetlandsWetlands remaining wetlandsLossesNet changeNet carbon stock change in dead wood per areaNet carbon stock change in litter per areaNet carbon stock change in dead organic matter per areaNet carbon stock change in soils per areaMineralsoilsOrganicsoilsLand converted to wetlandsSettlements Settlements remaining settlementsLand converted to settlementsOther landOther land remaining other landLand converted to other landHarvested wood productsOOOOOOOOOOOOOOOOOOOOOO,PNANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA,MNA,MNA,MGCGCGCGCGCGL,MGL,MGL,MGL,MGL,MGCGCGCGL,MGL,MGL,MGL,MGL,MGCGCGL,MMGCGCGCGCGCGCGCGCGCGCMFGL,MMGCGCGL,MMNCNCMMGCGCGCGCGGCGCGCGCGCGCFNCNCNCMMMNCNCNCGCGCMMGCFT3T2T1/T2T1Note:NA=not applicable.NC=not covered by any model.GL=GLOBIOM-China;M=MAgPIE-China;GC=GCAM5.3;F=FABLE Calculator;O=ORCHIDEE;and P=PECE-LIU.The methodological codes T1,T2 and T3 represent Tier 1,Tier 2 and Tier 3 methods respectively.FGCGCNCNCNCNCNCNCNCNCMMMNCNCNCNCNCNCNCNCNCNCNCNCNCNCMModelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations204.3.Coverage of LULUCF in models versus the National GHG InventoryAs none of the models reviewed cover all land use types,it is important to verify whether they cover the land use categories that account for the current main sources of GHG emissions and removals in China.In other words,have the models so far focused on the most important land use sectors for GHG emissions?Furthermore,are there large differences between the tier methods used for the development of Chinas 2014 National GHG Inventory,37 and those used by the modelling tools?When comparing the models coverage with Chinas main sources of emissions and removals(Table 4),overall the models do provide good coverage of most of the main current sources(forest land,grassland and cropland),with the exception of harvested wood products.Only one model covers harvested wood products(ORCHIDEE),and only one model covers wetlands(MAgPIE-China),both of which are relatively large sinks and for which country-specific emissions factors for China have been developed for the national GHG inventories.Interestingly,four out of the six models cover the other land use category,even though it is not of high importance in China for current GHG emissions and removals.However,including this land use category in the models does provide a more complete representation of current land use and land use transitions.Therefore,from a modelling perspective it makes sense to include this category.4.4.Coverage of agricultural non-CO2 emissions versus the National GHG InventoryWe collected data from six models that account for non-CO2 emissions from the agricultural sector(GLOBIOM-China,MAgPIE-China,GCAM5.3,FABLE Calculator-China,and AGHG-INV,and SRNM).All except SRNM consider major agricultural sources for non-CO2 greenhouse gas emissions,and there are substantial similarities across these five models(Table 5 and Table 6).They have all adopted the Table 4.Comparison of coverage of land-use categories and CO2 sources and sinks by the reviewed models and the Chinese 2014 National GHG InventoryLand use categoryCoverage by modelsTiermethodTiermethodEmission factors2014 emissions and removals(kt)Forest landT1/T2/T3T2CS-837,730 6 of 6 Reviewed modelsMethodologies used for the 2014 National GHG Inventory38CroplandT1/T2/T3T3CS-49,4605 of 6 GrasslandT1/T2/T3T2CS-109,160 5 of 6 WetlandsT1T2CS-44,540 1 of 6 SettlementsT1/T2T2CS2,530 2 of 6 Other landT1/T2/T3T1D0 4 of 6Harvested wood productsT1T2CS-110,5501 of 6Note:The methodological codes T1,T2 and T3 represent Tier 1,Tier 2 and Tier 3 methods respectively.CS=country-specific emission factors for China;D=default IPCC emission factors.*It should be noted that no separation is being made between models that have full or partial coverage of a land-use category.For example,FABLE Calculator-China only accounts for afforested land during the timescale of the model run,not all forest land within China.See Table 2 for further information on the models coverage of different land-use types.Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations21IPCC inventory framework and have considered the most critical sources of non-CO2 GHG emissions from agriculture enteric fermentation,manure management,and rice cultivation for CH4 emissions;and enteric fermentation,manure management,and agricultural soils for N2O emissions.However,only AGHG-INV includes emissions from other large livestock,such as camels,horses,and mules.GLOBIOM-China does not include N2O from the burning of agricultural residues.In addition to these five models,SRNM focuses on the variation of N2O emissions from agricultural land with nitrogen application,and only considers N2O emissions from agricultural soils and the partial discharge of manure(returned to the field as organic fertilizer).Across the different models,Tier 2 is predominantly used when calculating GHG emissions from the agriculture sector.China-specific emission factors are used in all five models for all significant animal species and agricultural land types.However,IPCC defaults are often used for some less substantial sources(e.g.,camels).Tier 3 methods are rarely used in this sector,particularly for models used to develop projections for the Chinese AFOLU sector.Over the past two decades,many studies have been conducted in China to monitor and evaluate the emission factors for rice cultivation,different types of drylands,and livestock and poultry breeding activities,which have laid a relatively good foundation for modelling studies.While some Tier 3 inventory studies have been conducted in China for rice cultivation and agricultural soils,they unfortunately encountered difficulties in conducting long-term emission scenario analyses.39Table 5.Comparison of CH4 sources and sink categories in the reviewed models and Chinas 2014 National GHG Inventory Activity categoryTier methodEmission factors2014 emissions(kt)Enteric fermentationMainly T1/T2Partly T3D,CS9,8565 of 6 Methodologies used for the 2014 National GHG Inventory40Manure managementT1/T2D,CS3,1555 of 6 Rice cultivationT1/T2/T3CS8,911 5 of 6 Agricultural soilsNANANA5 of 6 Field burning of agricultural residuesT1D,CS323 4 of 6 Note:The methodological codes T1,T2,and T3 represent Tier 1,Tier 2,and Tier 3 methods.CS=country-specific emission factors for China;D=default IPCC emission factors;NA=not applicable.Model coverageReviewed modelsTier methodT2T2T3NAT1Table 6.Comparison of N2O sources and sink categories in the reviewed models and Chinas National 2014 GHG InventoryActivity categoryTier methodEmission factors2014 emissions(kt)Manure managementT1/T2D,CS2336 of 6 Methodologies used for the 2014 National GHG Inventory41Agricultural soilsT1/T2D,CS9306 of 6 Field burning of agricultural residuesT1D,CS74 of 6 Note:The methodological codes T1,T2,and T3 represent Tier 1,Tier 2,and Tier 3 methods.CS=country-specific emission factors for China;D=default IPCC emission factors.Model coverageTier methodT2T2T1Reviewed models5.How do the model projections compare?Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations22Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations235.1.Projections for the LULUCF sectorFive modelling teams submitted their existing projections for the LULUCF sector for this assessment(GLOBIOM-China,MAgPIE-China,GCAM5.3,FABLE Calculator-China and PECE-LIU).The models vary substantially both in their starting points for projections(Table 7 and Table 8),and the expected development of the LULUCF sector(Figure 3).Regarding the level of GHG emissions and removals at the starting point of the projections(which vary from 2014 to 2016),only the PECE-LIU model showcases a level of GHG emissions and removals relatively consistent with Chinas latest 2014 National GHG Inventory.42 This is not surprising,as the PECE-LIU model has been calibrated against the National Forest Resources Inventory and Chinas National GHG Inventory data for 2006 and 2016.The GLOBIOM-China and GCAM5.3 models both indicate that the LULUCF sector is a net sink in 2015,but the size of the sink is much smaller than estimated in Chinas 2014 National GHG Inventory(Figure 3,Table 7).In contrast,the MAgPIE-China and FABLE Calculator-China models both indicate that the LULUCF sector is a net source of emissions in 2015.In the case of the MAgPIE-China model this is mainly driven by the large amount of emissions from grasslands(Figure 3,Table 7).Meanwhile,the FABLE Calculator-China only represents anthropogenic emissions and removals associated with land use change,and ignores emissions and removals associated with current land use.For example,the model accounts for sequestration from reforestation/afforestation events and the build-up of carbon sequestration over time,but does not account for fluxes to and from carbon pools in forest land or remaining forest land.Table 7.Comparison of annual GHG emission and removal estimates by the models and the 2014 National GHG Inventory(MtCO2eq per yr)Land-use category2014 National GHG Inventory43GLOBIOM-ChinaMAgPIE-ChinaGCAM5.3FABLE Calculator-ChinaPECE-LIUYearLULUCF total2014-1,115.9120152015133.22015-135.9820151852016-934-312.27Forest land-837.73-1,396.75-296-934-389.80Cropland-49.46-38.327NC77.53Grassland-109.161,346.59460NC0Wetlands-44.5453.40NCNCNCNCSettlements2.53NC15NCNCOther land0168.290NCNCHarvested wood products-110.55NCNCNCNCNCNote:NC=not covered.This signals that the model does not include a carbon pool for that land-use category.Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations24Given that GCAM5.3,GLOBIOM-China and MAgPIE-China all provide good coverage of the most important land use categories for historical GHG emissions and removals(Table 2 and Table 5),and use tier methodologies that are relatively consistent with Chinas national GHG inventories,the difference in reported values for the period 2010-2020 between the three models and the national GHG inventories is striking.The largest differences between the models and the national GHG inventories mainly relate to the representation of forest land,harvested wood products,cropland and grassland(Table 7).According to the 2014 National GHG Inventories for China,forest land represented a net sink of-837 MtCO2eq in 2014.However,the models estimates for the size of the sink vary between-1,469 and-389 MtCO2eq in 2015.As the area of forest land is relatively consistent between the models and the GHG inventories(Table 8),these discrepancies are likely to be explained by differences in the activity data,emission factors and data sources used to calculate the size of the sink(i.e.,forest growth functions,representation of different tree species,current annual increment,forest age structure,managed vs.unmanaged land etc.).Furthermore,the models likely use different data sources to represent historical afforestation and deforestation trends.As an example,historical afforestation and deforestation trends in GLOBIOM-China are commonly harmonized with the FAO FRA 2020 dataset,which may differ from the data being used for the preparation of the Chinese national GHG inventories reported to the UNFCCC.Table 8.Comparison of land-use area estimates in the models and the 2014 National GHG inventory(million hectares)Land-use category2014 National GHG Inventory44GLOBIOM-ChinaMAgPIE-ChinaGCAM5.3FABLE Calculator-ChinaPECE-LIUYearForest land2014180.04*20152015200.42015140.472015210.992016220.45211.92 Cropland135.06Grassland286.56NCWetlands39.73NCNCNCSettlements37.23NCNCOther land73.63*NCNote:For the model estimates,land areas are reported as total land.The figures in parentheses are the land areas assumed to remain neutral in terms of emissions and removals,i.e.there is a balance between carbon dioxide emissions emitted and absorbed carbon from the atmosphere in the area in a state of net-zero carbon dioxide emissions.NC=not covered(used when the model does not include a carbon pool for the land-use category).NR=not reported(used when the modelling team chose not to report the estimate).(NR)(102.06)(177)(0)(104.58)111.90116.00122.41148.88(NR)(NR)(122.11)(NR)NC374.94551.11411.85 205.96(NR)(456.47)(386.04)(94.57)NC3.35(NR)5.995.995.67(NR)(NR)(5.11)229.2 115.74192.31389.29(NR)(NR)(192.31)(389.29)*Based on the information provided in the 2014 National GHG inventory,forest land has been calculated as the sum of high,bamboo and open forests.Other land has been calculated as shrub land.Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations25In terms of the developments of net GHG emissions for the LULUCF sector,the GLOBIOM-China,FABLE Calculator-China and MAgPIE-China models all expect a decrease of net GHG emissions and a long-term increase in the removals for LULUCF sector(i.e.,by 2050/2060 as compared to 2000/2010):the expected reductions of net GHG emissions range from 109 MtCO2eq for MAgPIE-China,206 MtCO2eq for GLOBIOM-China and 266 MtCO2eq for the FABLE Calculator-China(Figure 3).The GCAM5.3 and PECE-LIU models project the LULUCF sector to remain a carbon sink overall,albeit with a slight sink reduction(between 34 and 120 MtCO2eq by 2060)compared to current levels.The difference in projections may relate to differences between the scenarios in terms of drivers of change,but also fundamental differences in model types,given the broad range of models being used.Figure 3:Historical and projected GHG emissions and removals for the LULUCF sector-400-600-800-1000-1200MtCO2 per yearLULUCF 2014 National GHG inventoryPECE-LIU,Updated NDC scenarioMAgPIE-China,BAU scenarioNote:Projections include scenarios from GLOBIOM-China,FABLE Calculator-China,MAgPIE-China,GCAM5.3,and PECE-LIU.Details of each scenario can be found in Annex II.Historical LULUCF GHG information has been compiled from PRC National Communication 201245 and PRC First and Second Biennial Update Report on Climate Change in 201646 and 2018.472000-20060040019902000201020202030204020502060GLOBIOM-ChinaFABLE Calculator-China,BAU scenarioGCAM5.3,p25n2060 scenarioModelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations26What is also striking in the projections for the LULUCF sector is that while the GLOBIOM-China,GCAM5.3 and MAgPIE-China models envisage a relatively smooth development of the LULUCF sink,the FABLE Calculator-China and the PECE-LIU models project strong fluctuations in the net emissions over time(Figure 3).PECE-LIU predicts a decline in LULUCF carbon sinks between 2016 and 2025,due to a reduction in the forest carbon stock growth rate and a decrease in the national afforestation rate.Note that in contrast to the other model projections,the PECE-LIU model projection incorporates Chinas latest land use and policy targets and assumes they will all be met.vi The scenario predicts that fulfilling these targets will result in a reduction in the forest stock growth rate between 2016 and 2025(compared to the previous reporting period),which will subsequently reduce forests carbon sink role by a substantial amount.In addition,the pace of afforestation in China is expected to slow down as the most suitable areas are already afforested,leading to future afforestation efforts shifting to more challenging areas within the country.5.2.Projections for the agriculture sector Based on the Chinese National GHG Inventory and model results(Figure 4),the agricultural sector produced 638 MtCO2eq of non-CO2 greenhouse gas emissions in 2000,and in 2020 these emissions increased to 950 MtCO2eq,ranging between 711 MtCO2eq and 1,118 MtCO2eq.The uncertainty range for non-CO2 GHG emissions is about 50%for the base year,and most models provided results from 2000 to 2050.In the SRNM estimates,only emissions from crop farming activities are included,so the results are a fraction of the other model estimates.However,they appear robust for that sub-category and are within 12%error of the national inventory and AGHG-INV models estimates of crop farming emissions(including rice cultivation and agricultural soils).In the business-as-usual(BAU)scenarios,most models project the non-CO2 emissions from Chinas agricultural sector to increase at a more moderate rate after 2030,and to peak between 2045 and 2060.The peak emission projections range from 760 MtCO2eq(FABLE-calculator)to 1,434 MtCO2eq(GLOBIOM-China)(Figure 4).These differences are explained by differences in base years,model structure,scenario assumptions,and parameter selection.The maximum mitigation potential averaged 400 MtCO2e per year(200 to 800 MtCO2eq)in 2050 across the models.The most optimistic mitigation estimation is from the GLOBIOM-China model,with a range of between 600 MtCO2eq and 900 MtCO2eq by 2050,in part reflecting its highest BAU emission(1,434 MtCO2eq in 2050).GCAM does not report a BAU scenario,but instead models two trajectories of Chinas agricultural GHG emissions under two different scenarios whereby national total greenhouse gas emissions peak in 2025 and in 2030.In the 2030-peaking scenario,Chinas agricultural GHG emissions are projected to peak at 957 MtCO2eq,whereas in the 2025-peaking scenario,they would peak at 895 MtCO2eq and then decline,with peak emissions 7%lower.The emissions in 2060,however,are quite similar for the two GCAM scenarios,at about 653 MtCO2eq.vi.Its projections are based on two main documents:one published by the Communist Party of China Central Committee and the State Council(2021)covering the period 2025 to 2030,and the other by the National Forestry and Grassland Administration(2019),covering 2035 to 2050.These include targets for the forest cover rate to reach 24.1%(ca.230 million hectares)and forest stock volume to reach 19 billion cubic metres.Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations27Figure 4:Historical and projected non-CO2 GHG emissions from Chinas agriculture,1990-2060800600100GHG emissions from Chinese agriculture(Mt CO2eq/yr)1000140012002000202020402060SRNM BAUAGHG_INV_BAUGLOBIOM NPiREFSRNM ONRAGHG_INV_TPGCAM5.3 p25n2060FABLE SUSTSRNM ALLGLOBIOM 200fMSFABLE-BAUSRNM CFWGLOBIOM 1200fBAUNational InventorySRNM DIETAGHG_INV_MTPGCAM5.3 p30n2060Note:Projections include scenarios from GLOBIOM-China,GCAM5.3,FABLE Calculator-China,AGHG-INV and SRNM(for agricultural N2O emissions only).Historical agricultural GHG information has been compiled from the PRC National Communication 201248 and PRC First and Second Biennial Update Report on Climate Change in 201649 and 201850.Details of each scenario can be found in Annex II.Shaded areas represent the mitigation potential as estimated by the different models.BAU and MS are the average across business as usual(BAU)projections and all mitigation scenarios(MS),but note that SRNM is not included in the calculation due to its partial coverage.6.Knowledge gaps and recommendations to improve models Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations28Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations29This assessment highlights significant uncertainties in existing model projections for historical and future LULUCF-related emissions and removals for China.The difference in historical net emissions between the models is striking(1,119 MtCO2eq,Figure 3).This uncertainty level is equivalent to the estimated size of the national GHG sink for China in 2014(1,150 MtCO2eq).A certain amount of variation between the models can be explained by the differences in scope,structure,input data,land use coverage,carbon pool coverage,activity data,and emission factors.There is still a lot of progress to be made in harmonizing and improving the models in order to align them better with the Chinese National GHG Inventory.Harmonization is an issue for numerous countries around the world,as noted and highlighted in the scientific literature.51 As a recommendation,as all countries will use the new common reporting tables(CRTs)to report their national GHG inventories no later than 2024,new routines and methodologies to harmonize their baselines and projections with the CRTs need to be developed.This review of the land use models for China reveals that a substantial number of them cover to some degree the categories of forest land(6 out of 6),cropland(5 out of 6),and grassland(5 out of 6)which are crucial for representing current levels of LULUCF emissions and related potential abatement,as seen from the Chinese National GHG Inventory of 2014.52 Nevertheless,there are limitations in their coverage.For example,only one model covers the harvested wood products carbon pool,which currently represents the second largest sink in the Chinese National GHG Inventory(-110.55 MtCO2eq).Furthermore,only two models currently cover settlements and only one covers wetlands.While neither of these land use categories currently represent significant sources of anthropogenic emissions and removals for the LULUCF sector in the Chinese National GHG Inventory,a considerable body of current literature points to their high mitigation potential and multiple co-benefits through activities such as protecting and restoring wetlands53 and developing green urban infrastructure.54,55 The inclusion of these land use categories and associated mitigation potentials in the different modelling tools would therefore be advantageous.Compared with other sectors,the LULUCF sector has a relatively high level of uncertainty surrounding estimates of anthropogenic GHG emissions.56,57,58 For example,in Chinas 2014 Inventory,the uncertainty levels for the various sectors were as follows:energy 5.2%;industrial process 3.9%;agriculture 19.8%;LULUCF 21.2%,and waste 23.2%.59 However,previous assessments have highlighted that methodological developments could significantly help to reduce the uncertainty levels for LULUCF estimates,60,61 including methods to identify areas of high priority for improvement.62 Countries seeking to enhance LULUCF GHG estimation methodologies can benefit from existing research and data collection to improve models where the uncertainty levels are highest.Based on our assessment,we highlight the following priority actions for improving modelling of the Chinese AFOLU sector:Increase collaboration in model development:Greater collaboration between model developers and national GHG inventory compilers would help to refine the data underlying the projections of future anthropogenic GHG emissions.Inter-model comparison,model validation,and collaborative efforts provide opportunities to reduce uncertainty and improve projections.Cross-sectoral comparisons in the AFOLU sector require more work,in particular reconciling models inconsistencies,harmonizing BAU assumptions and mitigation scenarios,as well as including estimates of the potential impact of policies related to the sector.Share and make data open access:Shared data sources are key for improving the calibration of models and reducing model uncertainty.By making datasets on emission factors and activities for the Chinese National GHG Inventory and by the different modelling teams freely available,not only will the calibrating of the models be enhanced,but the results of the models themselves will also be more transparent.Enable multi-model comparisons:Making detailed projections public,along with systematic mechanisms to compare different models and studies,will allow national and international stakeholders to better understand mitigation pathways,gain confidence in modelling results,and incorporate these findings into policymaking.Furthermore,for the development of technically feasible 1.5C compatible emission pathways for the AFOLU sector,and to develop pathways for the sector that are aligned with Chinas net neutrality target and the Paris Agreement,we urge policy makers to fund research activities that:Target the most uncertain areas:High uncertainty estimates for the AFOLU sector may reduce international confidence in emission reduction claims,especially for countries that expect forests and agriculture to contribute significantly to near-term reductions of GHG emissions.Include additional mitigation options and sectors with large-scale abatement potential:Further efforts to include neglected sectors(such as wetlands and settlements)in the various modelling tools can help eliminate many of the differences between models and thus allow for a more accurate assessment of future emissions in the AFOLU sector.Moreover,by incorporating low-cost but high-potential mitigation options into the various modelling frameworks,models can consider a more comprehensive set of mitigation options and help policy makers create more holistic mitigation pathways.Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations30Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations31Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations32GLOBIOM-ChinaThe Global Biosphere Management Model(GLOBIOM)is a recursive dynamic,vii spatially explicit,economic partial equilibrium model of the agriculture,forestry,and bioenergy sector with bilateral trade flows and costs.63 The model is built following a bottom-up setting based on detailed grid-cell information,providing the biophysical and technical cost information.The model computes a market equilibrium in 10-year time steps from 2000 to 2100 by maximising welfare(the sum of consumer and producer surplus)subject to technological,resource,and political constraints.In each step,market prices adjust endogenously to equalise supply and demand for each product and region.GLOBIOM-China was further developed with an enhanced representation of Chinas agricultural sector and environmental dynamics,a detailed validation,and confined assumptions following existing policies in China.64 MAgPIE-ChinaThe Model of Agricultural Production and its Impact on the Environment(MAgPIE)65,66,67,68 is a global agro-economic land system model which is connected to the grid-based dynamic vegetation model LPJmL,69,70 with a spatial resolution of 0.5 x 0.5.MAgPIE contains 12 world regions,in which countries are grouped together according to their geo-economic conditions.71 The model is run in a recursive dynamic mode over five-year intervals from 1995 to 2100.Agricultural production is endogenously determined in the optimisation,where the total cost of production is minimised for a given amount of regional food and bioenergy demand.The regional food demand is mainly driven by population and income growth.It takes into account regional economic conditions such as demand for agricultural commodities,technological development,and production costs,and spatially explicit data on potential crop yields land and water constraints.Based on these,the model derives spatially explicit land use patterns,yields,and total agricultural production costs for each grid cell.MAgPIE-China has been further developed to incorporate existing agriculture and environmental-related polices in China to improve the representation of Chinas AFOLU sector.72GCAM5.3The Global Change Analysis Model(GCAM)is an integrated tool for exploring the dynamics of the coupled human-Earth system and the response of this system to global changes.73,74 GCAM is a worldwide model representing the behaviour of and interactions between five systems:energy;water,agriculture,and land use;the economy,and the climate.Its energy-economy system currently operates for 32 regions globally,the land is divided into more than 300 subregions,and water is tracked for 233 basins.GCAM5.3s core operating principle is market equilibrium,and the representative agents in GCAM5.3 use the information on prices to interact with each other and make decisions about the allocation of resources.In GCAM5.3,modules on agriculture and land systems provide information about land use,land cover,carbon stocks and net emissions,the production of bioenergy,food,fibre,and forest products.Their demands are driven by the size of the population,their income levels,and commodity prices,and from these,land and GHG emissions are derived.75 The demand for bioenergy is driven by the energy sector,and agriculture and land systems module demand from water systems.Annex I:Model descriptionsvii.The“recursive dynamic”and“forward-looking dynamic”terminology refers to the solution approach of the model but it also implies different representations of the expectations of economic actors.In a recursive model,decisions about production,consumption and investment are made only on the basis of prices in the period of the decision,and this is often referred to as“myopic”expectations.Decisions are thus made as if costs and prices will remain unchanged in the future.In a forward-looking model,decisions today are being optimized over the full horizon,meaning that decisions today about production and consumption are based on expectations that are realized in the model simulation.Thus,economic actors are characterized as having“perfect”foresight they know exactly what will happen in the future in all periods of time covered by a modeling exercise.Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations33FABLE Calculator-ChinaThe Food,Agriculture,Biodiversity,Land use,and Energy(FABLE)Calculator is written in Excel and solved by ensuring equilibrium between the various uses(food,feed,processing)and domestic production minus exports plus imports,under a land availability constraint,for each five-year period over 2000-2050.76 It focuses on agriculture as the primary driver of land use change.It tests the impact of different policies and changes in the drivers of these systems through the combination of many scenarios,including population growth,dietary change,productivity growth,trade,loss and waste,climate change impacts,etc.It includes 76 raw and processed agricultural products from the crop and livestock sectors and relies extensively on the FAOSTAT(2022)database for input data.For every five-year interval over the period 2000-2050,the Calculator computes the level of agricultural activity,land use change,food consumption,trade,greenhouse gas emissions,water use,and biodiversity conservation.Market balance,agricultural land use and agricultural emissions are calibrated for the years 2000,2005 and 2010 using FAO statistics.The model is a national model in that it only represents development for China and has been modified to reflect the Chinese context,e.g.Chinas Cropland Protection Redline,Chinas target to achieve 26%forest cover rate by 2050,and historical changes in animal feed composition.77ORCHIDEEORCHIDEE(Organising Carbon and Hydrology In Dynamic Ecosystems)is the process-based land surface model of the French Institut Pierre Simon Laplace(IPSL),built for simulating carbon cycling in terrestrial ecosystems,and water and energy fluxes from site to global scale.78 The model can be driven by observation-based climate forcings,land cover and land use change maps(vegetation is presented as a series of plant functional types),and can potentially account for a series of natural and anthropogenic factors,such as fire,79 crops,80 grassland management,81,82 forest management,83 and bioenergy crops.84 ORCHIDEE is widely used for simulating global carbon,water and energy fluxes over terrestrial ecosystems and is one of the land models used for the annual Global Carbon Budget.The modelled carbon and water and energy dynamics(storage and fluxes)have been widely calibrated(for its parameters)and validated against various earth observations from site to global level,85 and from field measurement to remote sensing observations,sometimes with data assimilation systems(ORCHIDAS;https:/orchidas.lsce.ipsl.fr/).For future emissions from the AFOLU sector,the model can be driven by projections of climate from global climate models(GCMs)(e.g.,those in CMIP6)and projections of land use change(e.g.,crop/pasture expansion from integrated assessment models,IAMs),and can simulate future global terrestrial carbon cycles under different scenarios.The ORCHIDEE model is designed to be coupled to a global circulation model(such as LMDz within the IPSL-CM earth system model framework).86 It is set up so that atmospheric conditions affect the land surface processes,and the land surfaces in turn feed back to atmospheric conditions through changes in carbon,water and energy fluxes.Coupled land-atmosphere models thus offer the possibility of quantifying the climate effects of changes on the land surface and the impact of climate change on the land surface.ORCHIDEE has been calibrated and validated for terrestrial ecosystems in China,and used for assessing carbon,water,and energy fluxes.87,88,89,90,91,92,93PECE-LIU ModelThe Program of Energy and Climate Economics energy system model(PECE-LIU)is a bottom-up national energy-economy-environment system model with abundant technology details,of which the LULUCF module is focused on forest carbon sink estimation.94,95 The module is constructed based on the Stock-Difference method and the forestry carbon sink estimation framework from IPCC,which applies field forestry resources inventory data to calculate the national forest carbon pool and year-to-year changes,calibrated against the National Forest Resources Inventory and GHG Inventory data between 2006 Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations34and 2016.The model quantitatively simulates the development potential of forest carbon sinks in China from 2016 to 2060 with a policy-driven approach,using the near-term plan(e.g.the 14th Five-year plan),the carbon peaking and carbon neutral targets as important drivers to estimate future carbon sink development.The LULUCF module is further integrated with other modules of PECE-LIU to harmonize the dynamics of carbon sources and sinks in support of Chinas long-term low-carbon transition pathways.AGHG-INVThe Agriculture-induced non-CO2 GHG INVentory model(AGHG-INV)is a bottom-up modelviii with technology details.96,97 It can provide projections of non-CO2 GHG emissions from agricultural sources at the provincial level and is built on publicly available activity data from the national statistical database and the 2006 IPCC Guidelines for National Greenhouse Gas Inventories.98 In AGHG-INV,activity drivers for emission projections enter calculations externally using scenario data from different internationally and nationally recognised sources.Emission factors are consistent with historical levels in a business-as-usual(BAU)scenario,and emissions are a function of the projected activity level of major agricultural activities which reacts to animal feeding and crop farming,changed with national population size,urbanisation,economic development,and per capita consumption of major food products.The technical potential(TP)scenario evaluates the physical abatement potential of current best available technologies or practices,showing a conventional technology development path.The maximum technical potential(MTP)scenario evaluates the upper limit of physical mitigation for the Chinese agricultural sector without considering any technical,economic,or social implementation barriers across regions.SRNMThe Spatially Referenced Nonlinear Model(SRNM)is a global bottom-up model that simulates the variations in N2O emissions from agricultural land with nitrogen application(including synthetic fertilizer,livestock manure and crop residues)under various environmental conditions and agricultural management practices.99 Based on biogeochemistry,it assumes that the emission factor(EF)of N2O from agricultural land is a linear function of the applied nitrogen(N)rate,i.e.the EF-N relationship.In recent calculations,it assumes that there is a quadratic relationship between the applied nitrogen rate and EFs,by using the Bayesian Recursive Regression Tree Model.100 SRNM is driven by many input databases,including climate,soil properties,N inputs,irrigation use and the historic distribution of croplands.It can therefore capture the spatial heterogeneity of parameters such as climate,soil characteristics and crop management practices.101 The model is calibrated with N2O measurements from 153 peer-reviewed field studies in China,and validated by independent observations with chamber-based N2O flux observations from 180 global distributed sites outside China.102,103viii.A top-down approach looks at the system under examination as a whole and uses reduced form behavioural relationships with econometrical validation,while bottom-up approaches are developed from an engineering perspective and start from the sector of interest in detail before expanding the focus onto the whole system.Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations35Annex II:Data and scenario interpretation for Figure 4Annex.Data and scenario interpretation for Figure 4AbbreviationDescriptions National InventoryOfficial data from national inventory,only for 1994,2000,2005,2010,2014FABLE BAUBAU scenario for FABLE Calculator-China.It is characterized by a moderate population decrease,no constraints on agricultural expansion,a high afforestation target,medium productivity increases in the agricultural sector,an evolution towards higher consumption of animal products,and low livestock productivity increases.FABLE SUSTSustainable Pathway represents a future in which significant efforts are made to adopt sustainable policies and practices and corresponds to a high boundary of feasible action compared to the Current Trends.SRNM BAUIn the business as usual(BAU)scenario,current(the year 2017)policies and national plans were considered without any further policy interventions.To feed the growing population,crop production,sowing area and N input were predicted to increase.However,yield and agricultural management(including N rate,fertilizer types,fertilizer application methods,tillage and irrigation methods)were kept the same as in 2017,and climate factors changed with future climate scenario(RCP2.6)provided by the Inter-Sectoral Impact Model Intercomparison Project.SRNM ONROptimizing N fertilizer rate(ONR)scenario assumed the projections to be the same as BAU,except the total N input and N rate will dramatically decrease to improve agricultural N use efficiency and control N pollution.SRNM DIETChanging dietary guidelines(DIET)scenario,all projections were assumed to be the same as BAU,while the crop production would shift to optimizing human dietary structure.The DIET scenario assumes that the corresponding demand of maize for animal feed would reduce by 16-34%,which is in accordance with Chinese dietary guidelines.SRNM CFWAll input data in cutting food waste(CFW)scenario were the same as BAU,but crop production was assumed to decline in order to achieve the UN Global Sustainable Development Goals,driven by the reduced 50%food loss and waste in 2030,2040 and 2050 compared with 2017.SRNM ALLALL scenario was a package of ONR,DIET and CFW,which assumed that N inputs and N rate were the same as those in ONR scenario,meanwhile crop production met the projections of scenarios DIET and CFW.AGHG_INV BAUIn BAU,the projected level of agricultural activity was tied to changes in the population size,urbanization,economic development,and per capita diet.The production efficiency,EFs and technology of Chinas agricultural sector remain constant over time without considering further development and diffusion of mitigation policies or technologies.AGHG_INV TPThe technical potential(TP)scenario evaluates the physical abatement potential of current best available technologies or practices,showing a conventional technology development path.AGHG_INV MTPMaximum Technical Potential(MTP)scenario evaluates the upper limit of physical mitigation for the Chinese agricultural sector,without the consideration of any technical,economic or social barriers of implementation across regions.GLOBIOM 1200fCarbon Budget:1200 Gt CO2,Budget schemes:Full-century budgetGLOBIOM 200fCarbon Budget:200 Gt CO2,Budget schemes:Full-century budgetGLOBIOM 700fCarbon Budget:700 Gt CO2,Budget schemes:Full-century budgetGLOBIOM NPiREFGLOBIOM-China BAU scenarioGCAM5.3 p25n2060CO2 emissions peak in 2025,and GHG emissions reach net zero in 2060GCAM5.3 p30n2060CO2 emissions peak in 2030,and GHG emissions reach net zero in 2060Note:FABLE:The Food,Agriculture,Biodiversity,Land-use,and Energy Calculator GCAM5.3:Global Change Analysis Model GLOBIOM-China:Global Biosphere Management Model AGHG-INV:Agriculture-induced non-CO2 GHG INVentory model SRNM:Spatially Referenced Nonlinear ModelModelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations361.IPCC.(2014).Climate Change 2014:Mitigation of Climate Change.Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Edenhofer,O et al.(eds.).Cambridge University Press.2.IPCC.(2019).Climate Change and Land:An IPCC special report on climate change,desertification,land degradation,sustainable land management,food security,and greenhouse gas fluxes in terrestrial ecosystems P.R.Shukla,et al,(eds.).Cambridge University Press.3.Friedlingstein,P.,OSullivan,M.,Jones,M.W.,Andrew,R.M.,Hauck,J.,Olsen,A.,Peters,G.P.,Peters,W.,Pongratz,J.,Sitch,S.and Le Qur,C.(2020).Global carbon budget 2020.Earth System Science Data,12(4),pp.3269-3340.4.IPCC.(2014).Climate Change 2014:Mitigation of Climate Change.Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Edenhofer,O et al.(eds.).Cambridge University Press.5.IPCC.(2019).Climate Change and Land:An IPCC special report on climate change,desertification,land degradation,sustainable land management,food security,and greenhouse gas fluxes in terrestrial ecosystems P.R.Shukla,et al,(eds.).Cambridge University Press.6.Le Qur,C.,Andrew,R.M.,Friedlingstein,P.,et al.(2018).Global Carbon Budget 2017.Earth System Science Data,10(1),405-448.7.Smith,P.,Martino,D.,Cai,Z.,Gwary,D.,Janzen,H.,Kumar,P.,McCarl,B.,Ogle,S.,OMara,F.,Rice,C.,Scholes,B.,Sirotenko,O.,Howden,M.,McAllister,T.,Pan,G.,Romanenkov,V.,Schneider,U.,Towprayoon,S.,Wattenbach,M.,Smith,J.(2008).Greenhouse gas mitigation in agriculture.Philosophical Transactions of the Royal Society B,363,789-813.8.van Dijk,M.,Morley,T.,Rau,M.L.and Saghai,Y.,2021.A meta-analysis of projected global food demand and population at risk of hunger for the period 20102050.Nature Food,2(7),pp.494-501.9.USEPA.(2012).Global Anthropogenic non-CO2 Greenhouse Gas Emissions:1990-2030.United States Environmental Protection Agency,EPA 430-R-12-006.10.Lamb,A.,Green,R.,Bateman,I.,Broadmeadow,M.,Bruce,T.,Burney,J.,Carey,P.,Chadwick,D.,Crane,E.,Field,R.,Goulding,K.,Griffiths,H.,Hastings,A.,Kasoar,T.,Kindred,D.,Phalan,B.,Pickett,J.,Smith,P.,Wall,E.,zu Ermgassen,E.K.H.J.,and Balmford,A.(2016).The potential for land sparing to offset greenhouse gas emissions from agriculture.Nature Climate Change,6,488-492.11.Frank,S.,Beach,R.,Havlik,P.,Valin,H.,Herrero,M.,Mosnier,A.,Hasegawa,T.,Creason,J.,Ragnauth S.,and Obersteiner,M.(2018).Structural change as a key component for agricultural non-CO2 mitigation efforts.Nature Communication,9,1060.12.Roe,S.,Streck,C.,Beach,R.,Busch,J.,Chapman,M.,Daioglou,V.,Deppermann,A.,Doelman,J.,EmmetBooth,J.,Engelmann,J.and Fricko,O.(2021).Landbased measures to mitigate climate change:Potential and feasibility by country.Global Change Biology,27(23),6025-6058.13.Chen,M.and Forsell,N.(2020)Agriculture,forestry and other land use sector.In:Chinas New Growth Pathway:From the 14th Five Year Plan to carbon neutrality.Energy Foundation,Beijing.14.Chen,M.and Forsell,N.(2020)(ibid.).ReferencesModelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations3715.Chen,M.and Forsell,N.(2020)(ibid.).16.National Bureau of Statistics.(2022).National Data Platform.http:/ and Agriculture Data.http:/www.fao.org/faostat/en/18.FAO.(2022).China at a Glance.Food and Agriculture Organization of the United Nations.https:/www.fao.org/china/fao-in-china/china-at-a-glance/en/19.Cao,K.H.,and Birchenall,J.A.(2013).Agricultural productivity,structural change,and economic growth in post-reform China.Journal of Development Economics,104,165-180.20.Yu,J.L.,Wu J.(2018).The sustainability of agricultural development in China:The agriculture-environment nexus.Sustainability,10,1776.21.Zhao,H.,Chang,J.F.,Havlik,P.,et al.(2021).Chinas future food demand and its implications for trade and environment.Nature Sustainability,4:1042-1051.22.National Bureau of Statistics.(2022).National Data Platform.http:/https-data-stats-gov-cn-774 Bureau of Statistics.(2022)(ibid.)24.AGFEP.(2021).2021 China and global food policy report:Rethinking agrifood systems for the post-COVID world.Academy of Global Food Economics and Policy(AGFEP),China Agricultural University.Beijing.http:/ Forsell,N.(2020)(ibid.)27.Ni,H.X.,Lyu,X.D.(2018).Understanding the gap between the competitiveness of Chinese agricultural products and those of other countries.Agriculture Newsletter.(10):59-61.28.AGFEP.(2022).2022 China and global food policy report:Reforming agricultural support policy for transforming agri-food systems.Academy of Global Food Economics and Policy(AGFEP),China Agricultural University.Beijing.http:/ al.(2021).Chinas future food demand and its implications for trade and environment.Nature Sustainability,4:1042-1051.31.FAOSTAT.(2022).Food and Agriculture Data.http:/www.fao.org/faostat/en/32.Government of China(2018).The Peoples Republic of China Second Biennial Update Report on Climate Change.https:/www4.unfccc.int/sites/SubmissionsStaging/NationalReports/Documents/23146085_China-BUR2-1-China_BUR2_English.pdf33.Government of China.(2016).The Peoples Republic of China First Biennial Update Report on Climate Change.https:/unfccc.int/sites/default/files/resource/chnbur1.pdf34.Government of China.(2018).The Peoples Republic of China Third National Communication on Climate Change.https:/unfccc.int/sites/default/files/resource/China 3NC_English_0.pdfModelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations3835.Government of China(2018).(ibid.)36.Government of China(2018).(ibid.)(Table 2)37.Government of China(2018).(ibid.)38.Government of China(2018).(ibid.)(Table 4)39.Zhou,F.,Shang,Z.,Ciais,P.,Tao,S.,Piao,S.,Raymond,P.,He,C.,Li,B.,Wang,R.,Wang,X.,Peng,S.,Zeng,Z.,Chen,H.,Ying,N.,Hou,X.,Xu,P.(2014).A new high-resolution N2O emission inventory for China in 2008.Environmental Science&Technology,48,8538-8547.40.Government of China(2018).(ibid.)(Table5)41.Government of China(2018).(ibid.)(Table6)42.Government of China(2018).(ibid.)43.Government of China(2018).(ibid.)(Table 7)44.Government of China(2018).(ibid.)(Table 8)45.Government of China.(2012).Second National Communication on Climate Change of The Peoples Republic of China.https:/unfccc.int/sites/default/files/resource/The Second National Communication on Climate Change of P. R. China.pdf46.Government of China.(2016).The Peoples Republic of China First Biennial Update Report on Climate Change.https:/unfccc.int/sites/default/files/resource/chnbur1.pdf47.Government of China.(2018).(ibid.)48.Government of China.(2012).Second National Communication on Climate Change of The Peoples Republic of China.https:/unfccc.int/sites/default/files/resource/The Second National Communication on Climate Change of P. R. China.pdf49.Government of China.(2016).(ibid.)50.Government of China.2016.(ibid.)51.Grassi,G.,Stehfest,E.,Rogelj,J.,Van Vuuren,D.,Cescatti,A.,House,J.,Nabuurs,G.J.,Rossi,S.,Alkama,R.,Vias,R.A.and Calvin,K.,2021.Critical adjustment of land mitigation pathways for assessing countries climate progress.Nature Climate Change,11(5),pp.425-434.52.Government of China(2018).(ibid.)53.Roe,S.,Streck,C.,Obersteiner M.,Frank,S.,Griscom,B.,Drouet,L.,Fricko,O.,Gusti,M.,Harris,N.,Hasegawa,T.,Hausfather,Z.,Havlik,P.,House,J.,Nabuurs,G.,Popp,A.,Sanchez M.J.S.,Sanderman,J.,Smith,P.,Stehfest,E.,and Lawrence,D.(2019).Contribution of the land sector to a 1.5 degree world.Nature Climate Change,9,817-828.54.Chen,W.Y.(2015).The role of urban green infrastructure in offsetting carbon emissions in 35 major Chinese cities:A nationwide estimate.Cities,44,112-120.55.Song,X.P.,Tan,P.Y.,Edwards,P.,and Richards,D.(2018).The economic benefits and costs of trees in urban forest stewardship:A systematic review.Urban Forestry&Urban Greening,29,162-170.56.Jonas,M.,Marland,G.,Krey,V.,Wagner,F.and Nahorski,Z.(2014).Uncertainty in an emissions-constrained world.Climatic Change,124(3),459476Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations3957.Pulles,T.(2017).Did the UNFCCC review process improve the national GHG inventory submissions?Carbon Management,8(1):193158.Friedlingstein,P.,Jones,M.W.,OSullivan,M.,Andrew,R.M.,Bakker,D.C.,Hauck,J.,Le Qur,C.,Peters,G.P.,Peters,W.,Pongratz,J.and Sitch,S.(2022).Global carbon budget 2021.Earth System Science Data,14(4),pp.1917-200559.Government of China(2018).(ibid.)60.McRoberts,R.E.,Tomppo,E.O.and Nsset,E.(2010).Advances and emerging issues in national forest inventories.Scandinavian Journal of Forest Research,25(4),368-381.61.Sthl,G.,Heikkinen,J.,Petersson,H.,Repola,J.and Holm,S.,2014.Sample-based estimation of greenhouse gas emissions from forestsa new approach to account for both sampling and model errors.Forest Science,60(1),pp.3-13.62.McGlynn,E.,Li,S.,F Berger,M.,Amend,M.,&L Harper,K.(2022).Addressing uncertainty and bias in land use,land use change,and forestry greenhouse gas inventories.Climatic Change,170(1),1-25 63.Havlk,P.,Valin,H.,Gusti,M.,Schmid,E.,Leclere,D.,Forsell,N.,Herrero,M.,Khabarov,N.,Mosnier,A.,Cantele,M.,and Obersteiner,M.(2015).Climate Change Impacts and Mitigation in the Developing World,An Integrated Assessment of the Agriculture and Forestry Sectors.Background Paper for the World Bank Report:“Shock Waves:Managing the Impacts of Climate Change on Poverty.”Policy Research Working Paper WPS7477.The World Bank,Washington DC.64.Zhao,H.,Chang,J.F.,Havlik,P.,et al.(2021).Chinas future food demand and its implications for trade and environment.Nature Sustainability,4:1042-1051.65.Lotze-Campen,H.,Mller,C.,Bondeau,A.,Rost,S.,Popp,A.and Lucht,W.(2008).Global food demand,productivity growth,and the scarcity of land and water resources:a spatially explicit mathematical programming approach.Agricultural Economics,39(3),pp.325-338.66.Popp,A.,Humpender,F.,Weindl,I.,Bodirsky,B.L.,Bonsch,M.,Lotze-Campen,H.,Mller,C.,Biewald,A.,Rolinski,S.,Stevanovic,M.,Dietrich,J.P.(2014).Land use protection for climate change mitigation.Nature Climate Change,4,10951098.https:/doi.org/10.1038/nclimate244467.Dietrich,J.P.,Bodirsky,B.L.,Humpender,F.,Weindl,I.,Stevanovi,M.,Karstens,K.,Kreidenweis,U.,Wang,X.,Mishra,A.,Klein,D.,Ambrsio,G.,Araujo,E.,Yalew,A.W.,Baumstark,L.,Wirth,S.,Giannousakis,A.,Beier,F.,Chen,D.M.-C.,Lotze-Campen,H.,Popp,A.(2019).MAgPIE 4 a modular open-source framework for modelling global land systems.Geoscientific Model Development,12,12991317.https:/doi.org/10.5194/gmd-12-1299-201968.Wang,X.,Dietrich,J.P.,Lotze-Campen,H.,Biewald,A.,Stevanovi,M.,Bodirsky,B.L.,Brmmer,B.,Popp,A.(2020).Beyond land use intensity:Assessing future global crop productivity growth under different socioeconomic pathways.Technological Forecasting and Social Change 160,120208.https:/doi.org/10.1016/j.techfore.2020.12020869.Bondeau,A.,Smith,P.,Zaehle,S.,Schaphoff,S.,Lucht,W.,Cramer,W.,Gerten,D.,Lotze-Campen,H.,Mller,C.,Reichstein,M.,Smith,B.(2007).Modelling the role of agriculture for the 20th century global terrestrial carbon balance.Global Change Biology 13,679706.70.Schaphoff,S.,von Bloh,W.,Ramming,A.,Thonicke,K.,Biemans,H.,Forkel,M.,Gerten,D.,Heinke,J.,Jagermeyr,J.,Knauer,J.,Langerwisch,F.,Lucht,W.,Muller,C.,Rolinski,S.,Waha,K.(2018).LPJmL4 a dynamic global vegetation model with managed land Part 1:Model description.Geoscientific Model Development,11,33,https:/doi.org/10.5194/gmd-11-1343-2018Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations4071.Dietrich,J.P.,Bodirsky,B.L.,Humpender,F.,Weindl,I.,Stevanovi,M.,Karstens,K.,Kreidenweis,U.,Wang,X.,Mishra,A.,Klein,D.,Ambrsio,G.,Araujo,E.,Yalew,A.W.,Baumstark,L.,Wirth,S.,Giannousakis,A.,Beier,F.,Chen,D.M.-C.,Lotze-Campen,H.,Popp,A.(2019).MAgPIE 4 a modular open-source framework for modelling global land systems.Geoscientific Model Development,12,12991317.https:/doi.org/10.5194/gmd-12-1299-201972.Wang,X.,Xu,M.,Lin,B.,Bodirsky,B.L.,Xuan,J.,Dietrich,J.P,Stevanovi,M.,Bai,Z.,Shuqin,L.M.,Shenggen Fan,J.,Lotze-Campen,H.and Popp,A.(In revision).Reforming Chinas fertilizer policies:Implications for nitrogen pollution reduction and food security.73.Edmonds,J.A.,Wise,M.A.and MacCracken,C.N.(1994).Advanced Energy Technologies and Climate Change:An analysis using the global change assessment model(GCAM)(No.PNL-9798).Pacific Northwest National Lab,Richland,WA(United States).74.Calvin,K.,Patel,P.,Clarke,L.,Asrar,G.,Bond-Lamberty,B.,Cui,R.Y.,Vittorio,A.D.,Dorheim,K.,Edmonds,J.,Hartin,C.and Hejazi,M.(2019).GCAM v5.1:representing the linkages between energy,water,land,climate,and economic systems.Geoscientific Model Development,12(2),pp.677-698.75.Edmonds,J.A.,Wise,M.A.and MacCracken,C.N.(1994).Advanced Energy Technologies and Climate Change:An analysis using the global change assessment model(GCAM)(No.PNL-9798).Pacific Northwest National Lab,Richland,WA(United States).76.Mosnier,A.,Penescu,L.,Perez Guzman,K.,Steinhauser,J.,Thomson,M.,Douzal,C.,Poncet,J.(2020).FABLE Calculator 2020 update.International Institute for Applied Systems Analysis (IIASA)and Sustainable Development Solutions Network(SDSN),Laxenburg,Austria.https:/pure.iiasa.ac.at/id/eprint/16934/7/210108_FABLECalculator_Documentation_final_clean.pdf.77.Jin X.,Bai Z.,Zhao H.,Wang X.,Chang J.,Hua F.and Ma L.(2020),“Pathways to Sustainable Land Use and Food Systems in China by 2050”In:FABLE 2020,Pathways to Sustainable Land Use and Food Systems,2020 Report of the FABLE Consortium.Laxenburg and Paris:International Institute for Applied Systems Analysis(IIASA)and Sustainable Development Solutions Network(SDSN),pp.195-229.https:/doi.org/10.22022/ESM/12-2020.168978.Krinner,G.,Viovy,N.,de NobletDucoudr,N.,Oge,J.,Polcher,J.,Friedlingstein,P.,Ciais,P.,Sitch,S.,Prentice,I.C.(2005).A dynamic global vegetation model for studies of the coupled atmospherebiosphere system.Global Biogeochemical Cycles,19.https:/doi.org/10.1029/2003GB00219979.Yue,C.,Ciais,P.,Cadule,P.,Thonicke,K.,Archibald,S.,Poulter,B.,Hao,W.M.,Hantson,S.,Mouillot,F.,Friedlingstein,P.,Maignan,F.,and Viovy,N.(2014).Modelling the role of fires in the terrestrial carbon balance by incorporating SPITFIRE into the global vegetation model ORCHIDEE Part 1:simulating historical global burned area and fire regimes.Geosci.Model Dev.7,2747-2767.80.Wu,X.,Vuichard,N.,Ciais,P.,Viovy,N.,de Noblet-Ducoudr,N.,Wang,X.,Magliulo,V.,Wattenbach,M.,Vitale,L.,Di Tommasi,P.,Moors,E.J.,Jans,W.,Elbers,J.,Ceschia,E.,Tallec,T.,Bernhofer,C.,Grnwald,T.,Moureaux,C.,Manise,T.,Ligne,A.,Cellier,P.,Loubet,B.,Larmanou,E.,and Ripoche,D.(2016).ORCHIDEE-CROP(v0),a new process-based agro-land surface model:model description and evaluation over Europe.Geosci.Model Dev.9,857-873.81.Chang,J.,Ciais,P.,Gasser,T.,Smith,P.,Herrero,M.,Havlk,P.,Obersteiner,M.,Guenet,B.,Goll,D.S.,Li,W.,Naipal,V.,Peng,S.,Qiu,C.,Tian,H.,Viovy,N.,Yue,C.,Zhu,D.(2021).Climate warming from managed grasslands cancels the cooling effect of carbon sinks in sparsely grazed and natural grasslands.Nature Communications 12,118.Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations4182.Chang,J.F.,Viovy,N.,Vuichard,N.,Ciais,P.,Wang,T.,Cozic,A.,Lardy,R.,Graux,A.I.,Klumpp,K.,Martin,R.,Soussana,J.F.(2013).Incorporating grassland management in ORCHIDEE:model description and evaluation at 11 eddy-covariance sites in Europe.Geoscientific Model Development,6,2165-2181.83.Naudts,K.,Ryder,J.,McGrath,M.J.,Otto,J.,Chen,Y.,Valade,A.,Bellasen,V.,Berhongaray,G.,Bnisch,G.,Campioli,M.and Ghattas,J.(2015).A vertically discretised canopy description for ORCHIDEE(SVN r2290)and the modifications to the energy,water and carbon fluxes.Geoscientific Model Development,8(7),pp.2035-2065.84.Li,W.,Yue,C.,Ciais,P.,Chang,J.,Goll,D.,Zhu,D.,Peng,S.,Jornet-Puig,A.(2018).ORCHIDEE-MICT-BIOENERGY:an attempt to represent the production of lignocellulosic crops for bioenergy in a global vegetation model.Geoscientific Model Development,11,2249-2272.85.Krinner,G.,Viovy,N.,de NobletDucoudr,N.,Oge,J.,Polcher,J.,Friedlingstein,P.,Ciais,P.,Sitch,S.,and Prentice,I.C.(2005).A dynamic global vegetation model for studies of the coupled atmospherebiosphere system.Global Biogeochemical Cycles,19.https:/doi.org/10.1029/2003GB002199.86.Remaud,M.,Chevallier,F.,Cozic,A.,Lin,X.and Bousquet,P.(2018).On the impact of recent developments of the LMDz atmospheric general circulation model on the simulation of CO2 transport.Geoscientific Model Development,11(11),pp.4489-4513.87.Tan,K.,Ciais,P.,Piao,S.,Wu,X.,Tang,Y.,Vuichard,N.,Liang,S.and Fang,J.(2010).Application of the ORCHIDEE global vegetation model to evaluate biomass and soil carbon stocks of QinghaiTibetan grasslands.Global Biogeochemical Cycles,24(1).88.Piao,S.,Yin,G.,Tan,J.,Cheng,L.,Huang,M.,Li,Y.,Liu,R.,Mao,J.,Myneni,R.B.,Peng,S.and Poulter,B.(2015).Detection and attribution of vegetation greening trend in China over the last 30 years.Global Change Biology,21(4),1601-1609.89.Piao,S.,Fang,J.,Ciais,P.,Peylin,P.,Huang,Y.,Sitch,S.,Wang,T.(2009).The carbon balance of terrestrial ecosystems in China.Nature,458,1009-101390.Wang,T.,Ottle,C.,Boone,A.,Ciais,P.,Brun,E.,Morin,S.,Krinner,G.,Piao,S.and Peng,S.(2013).Evaluation of an improved intermediate complexity snow scheme in the ORCHIDEE land surface model.Journal of Geophysical Research:Atmospheres,118(12),6064-6079.91.Wang,X.,Ciais,P.,Li,L.,Ruget,F.,Vuichard,N.,Viovy,N.,Zhou,F.,Chang,J.,Wu,X.,Zhao,H.and Piao,S.(2017).Management outweighs climate change on affecting length of rice growing period for early rice and single rice in China during 19912012.Agricultural and Forest Meteorology,233,1-11.92.Wang,Z.,Chang,J.,Peng,S.,Piao,S.,Ciais,P.and Betts,R.(2019).Changes in productivity and carbon storage of grasslands in China under future global warming scenarios of 1.5 C and 2 C.Journal of Plant Ecology,12(5),pp.804-81493.Yin,Z.,Wang,X.H.,Ottl,C.,Zhou,F.,Guimberteau,M.,Polcher,J.,Peng,S.S.,Piao,S.L.,Li,L.,Bo,Y.and Chen,X.L.(2020).Improvement of the irrigation scheme in the ORCHIDEE land surface model and impacts of irrigation on regional water budgets over China.Journal of Advances in Modelling Earth Systems,12(4),p.e2019MS001770.94.Energy Foundation China.(2020).Synthesis Report 2020 on Chinas Carbon Neutrality:Chinas New Growth Pathway:from the 14th Five Year Plan to Carbon Neutrality.Energy Foundation China,Beijing.Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations4295.Liu,J.,Yin,M.,Xia-Hou,Q.,Wang,K.,Zou J.(2021).Comparison of sectoral low-carbon transition pathways in China under the nationally determined contribution and 2 C targets.Renewable and Sustainable Energy Reviews,2021,149:11133696.Energy Foundation China.(2020).Synthesis Report 2020 on Chinas Carbon Neutrality:Chinas New Growth Pathway:from the 14th Five Year Plan to Carbon Neutrality.Energy Foundation China,Beijing.97.Chen,M.,Cui,Y.,Jiang,S.,Forsell,N.(2022).Toward carbon neutrality before 2060:trajectory and technical mitigation potential of non-CO2 greenhouse gas emissions from Chinese agriculture.Journal of Cleaner Production.133186.98.IPCC.(2008).2006 IPCC Guidelines for National Greenhouse Gas Inventories A primer.Prepared by the National Greenhouse Gas Inventories Programme,Eggleston H.S.,Miwa K.,Srivastava N.and Tanabe K.(eds).IGES,Japan.99.Wang,Q.,Zhou,F.,Shang,Z.,et al.(2020).Data-driven estimates of global nitrous oxide emissions from croplands.National Science Review,7(2),441-452.100.Zhou,F.,Shang,Z.,Zeng,Z.,et al.(2015).New model for capturing the variations of fertilizer-induced emission factors of N2O.Global Biogeochemical Cycles,29(6),885-897101.Shang,Z.,Zhou,F.,Smith,P.,Saikawa,E.,Ciais,P.,Chang,J.,Tian,H.,del Grosso,S.J.,Ito,A.,Chen,M.,Wang,Q.,Bo,Y.,Cui,X.,Castaldi,S.,Juszczak,R.,Kasimir,A.,Magliulo,V.,Medinets,S.,Medinets,V.,Rees,R.M.,Wohlfahrt,G.,Sabbatini,S.(2019).Weakened growth of cropland-N2O emissions in China associated with nationwide policy interventions.Global Change Biology,25(11),3706-3719.102.Cui,X.,Zhou,F.,Ciais,P.,et al.(2021).Global mapping of crop-specific emission factors highlights hotspots of nitrous oxide mitigation.Nature Food,2(11),886-893.103.Cui,X.,Shang,Z.,Xia,L.,et al.(2022).Deceleration of cropland-N2O emissions in China and future mitigation potentials.Environmental Science&Technology,56(7),4665-4675.Modelling greenhouse gas fluxes from Chinas agriculture,forestry and land use sector:Gaps and recommendations44Modelling Greenhouse Gas Fluxes from Chinas Agriculture,Forestry and Land Use Sector:Gaps and recommendationsMay 2023
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