《中国科学院:2023地球大数据支撑可持续发展目标报告(英文版)(127页).pdf》由会员分享,可在线阅读,更多相关《中国科学院:2023地球大数据支撑可持续发展目标报告(英文版)(127页).pdf(127页珍藏版)》请在三个皮匠报告上搜索。
1、PrefaceExecutive SummaryIntroductionSDG 2 Zero Hunger14 Midterm Progress15 Monitoring of Sustainable Food Production SystemsGlobal Monitoring and Evaluation of Cropland Changes 15Distribution of Terraced Fields in China and Their Soil Conservation Benefits 1718 Evaluation of Sustainable Food Product
2、ion Policies and Benefit Chinas Cropland Utilization and Protection Policies and Experience Sharing 18Evaluation of Chinas Cropland Construction Benefits 20SDG 6 Clean Water and Sanitation26 Midterm Progress27 Safe Drinking Water and SanitationAssessment of Water Quality Monitoring in Chinas Drinkin
3、g Water Sources 27Monitoring the Proportion of Population Covered by Public Sanitation Facilities in China 2829 Improving Water EnvironmentSpatiotemporal Change in Transparency of Global Large Lakes and Reservoirs 2930 Improving Water-Use EfficiencyGlobal Cropland Water-Use Efficiency Changes 3033 C
4、hanges in Water Ecosystems Surface Water Area Changes of Global natural Lakes and Reservoirs 33Changes in Groundwater Storage in Africa 35SDG 7 Affordable and Clean Energy39 Midterm Progress40 Clean CookingProportion of Chinese Population Relying on Clean Cooking Energy and Technologies 4041 Renewab
5、le EnergyGlobal Wind and Solar Resource 41Chinas Clean Energy Transition 43Ultra-High-Voltage Transmission of Renewable Energy over Long Distance in China 4545 International Energy CooperationChinas International Cooperation on Clean Energy 46SDG 11 Sustainable Cities and Communities51 Midterm Progr
6、ess52 Urban Public TransportGlobal Proportion and Variation of Population with Access to Convenient Public Transport in Major Cities 5254 Heritage Conservationnatural and Mixed World Heritage Sites Open Boundary Data Quality Improvement 5456 Urban EnvironmentGlobal Temporal and Spatial Trends of Atm
7、ospheric Particulate Matter 56Long-Term High-Resolution PM2.5 Retrieval and Trends in China 5758 SDG 11 Comprehensive AssessmentUrban Assessment and Sustainable Development Goals 5804050812372349CONTENTSSDG 13 Climate Action63 Midterm Progress64 Reducing the Impact of Climate-related DisastersAnalys
8、is of the Impact of Flood Disasters and Defense Effectiveness in China 6466 Actively Addressing Climate ChangeChinas Climate Change Strategies and Actions 66Global Real-Time Carbon Emissions 67Greenhouse Gas(CO2,CH4,and n2O)Fluxes in Global Terrestrial Ecosystems 6971 Strengthening Climate Change Ed
9、ucationSocial Media Communication on Climate Change in China 71SDG 14 Life below Water75 Midterm Progress76 Reducing Marine PollutionAnalysis of the Distribution Characteristics and Temporal Variation of Microplastics in Chinas Coastal Waters 7678 Preserving Marine EcosystemsDynamic Monitoring of Se
10、agrass Meadow Distribution in Coastal China 78Dynamic Monitoring of Live Coral Cover in Typical Coral Reefs 80Comprehensive Assessment of Typical Coastal Bay Ecosystem Health in China 8182 Sustainable Management of AquacultureDynamic Remote Sensing Monitoring and Analysis of Raft Culture in Chinas C
11、oastal Waters 82SDG 15 Life on Land87 Midterm Progress88 Forest Conservation and RestorationDynamic Change in Forest Cover and Aboveground Biomass 88Global Spatial Distribution of Oil Palm 8991 Land Degradation NeutralityDynamic Changes Towards LDn in China 91Dynamic Change in Sand and Dust Storms i
12、n the Mongolian Plateau and Response Strategies 9394 Red List IndexChinas Red List Index for Higher Plants 9495 Invasive Alien SpeciesPrediction and Assessment of Distribution of Key Agricultural Invasive Pests 9597 Comprehensive Demonstration of Ecosystem RestorationEcological Restoration Achieveme
13、nts and Experience of Saihanba Mechanized Forest Farm in Hebei 97Integrated Evaluations and Interactions among SDGs100 Integrated and Interaction Analysis of SDGs in Cities at or above the Prefecture Level of ChinaIntegrated Assessment of Progress on SDGs 101Analysis of SDGs Synergies and Trade-offs
14、 103Spatial Spillover Effects for SDGs 104Projection of Future Development Scenarios for SDGs 105Summary and ProspectsAppendices111 Data Sources115 Acronyms&Abbreviations117 References121 Core Team of Authors38504Big Earth Data in Support of the Sustainable Development Goals(2023)From 201
15、5 to the present,the implementation of the United nations Transforming our world:the 2030 Agenda for Sustainable Development(referred to as the 2030 Agenda)has reached its halfway point.During this period,progress on the Sustainable Development Goals(SDGs)has encountered serious challenges and diffi
16、culties despite the overall advancements.The international community has gained deeper insights into long-term issues such as climate change and biodiversity loss,but further efforts are needed to enhance implementation.Unexpected events like the COVID-19 pandemic and regional conflicts have had sig
17、nificant and far-reaching impacts on society,the economy,and the environment.It is important for the international community to learn both experience and lessons of the first half of the 2030 Agenda,so that we can enhance the implementation of the SDGs in the second half and explore directions for f
18、uture sustainable development.The slow progress during the initial phase underscores the global inadequacy in addressing long-term risks as well as short-term crises in the process of promoting sustainable development.The lack of timely and accurate data remains a weakness in our response to both lo
19、ng-term and short-term issues and is a bottleneck hindering the implementation and monitoring of the SDGs and the formulation of science-based decisions.In September 2021,Chinese President Xi Jinping proposed the Global Development Initiative(GDI)during the 76th United nations General Assembly.The G
20、DI aims to deepen international cooperation,accelerate the implementation of the 2030 Agenda and promote stronger,greener,and healthier development.It places a special emphasis on advancing collaboration in areas such as digital connectivity in the digital age to expedite the realization of the 2030
21、 Agenda.The GDI is committed to building a global community of development and a crucial foundation in this endeavor is the promotion of digital technology.Big Earth Data,as a representative form of such technology,can play a vital role in filling the gaps in current SDG statistical data and spatial
22、-temporal information.2023 marks the third anniversary of President Xi Jinpings announcement of the establishment of the International Research Center of Big Data for Sustainable Development Goals(CBAS)in China,and also the fifth consecutive year of publishing the report Big Earth Data in Support of
23、 the Sustainable Development Goals(referred to as the report).Over the past few years,the research team has made use of the increasingly refined Big Earth Data platform and leveraged the advantages of objective scientific data and rich spatial-temporal information to assess the midterm progress on s
24、ustainable development in China and globally.The teams focus has been on exploring the bottlenecks hindering sustainable development and identifying future development directions.This years report focuses on the midterm evaluation of sustainable development in China and globally through Big Earth Da
25、ta.It aims to further expand data products for SDG indicators,broaden the depth of indicator contents,complete progress evaluations of all environment-related SDG indicators in China,and actively provide public data products to serve all the countries,particularly developing countries in terms of ev
26、aluating the implementation of the 2030 Agenda.It also provides recommendations on future science-based decision-making,accelerating sustainable development processes,building big data acquisition capabilities,and adjusting and optimizing indicators.The report stands as a scientific support for the
27、implementation of the 2030 Agenda.The report is a collaborative effort,written by over 160 researchers from more than 50 organizations,including research institutes and universities in the field of sustainable development and big data.The project has received strong support from the Chinese Academy
28、of Sciences and various government departments,and the team members have dedicated significant efforts to this endeavor.We express our heartfelt gratitude to all those involved in making this report a reality.PrefaceGuo HuadongDirector of the International Research Center of Big Data for Sustainable
29、 Development GoalsMember of the Un 10-Member Group to support the TFM for SDGs(20182021)05ForewordExecutive SummaryThis report gives full play to the advantages and characteristics of Big Earth Data.It focuses on 25 targets related to seven Sustainable Development Goals(SDGs),including SDG 2(Zero Hu
30、nger),SDG 6(Clean Water and Sanitation),SDG 7(Affordable and Clean Energy),SDG 11(Sustainable Cities and Communities),SDG 13(Climate Action),SDG 14(Life Below Water),SDG 15(Life on Land),as well as the interactions among SDG indicators,resulting in 41 research cases.From three aspects:data products,
31、methods and decision-making,this report shows the results of monitoring and midterm evaluation of relevant SDG indicators at four scales:global,regional,national,and typical areas.The report represents an innovative practice of big data in support of SDG implementation and can provide scientific ref
32、erence for decision-makers.Regarding SDG 2(Zero Hunger),the report focuses on four indicators of two targets and conducts global/China midterm progress evaluations.The research findings indicate that China is moving towards sustainable food production,on the basis of progress towards meeting nurtiti
33、onal needs.The stunting rate of children under six(SDG 2.2.1)in China decreased from 8.1%in 2013 to 4.8%in 2017.The overweight rate of children under six(SDG 2.2.2)decreased from 8.4%in 2013 to 6.8%in 2017.The anemia prevalence among women of reproductive age(SDG 2.2.3)decreased from 15.0%in 2012 to
34、 14.5%in 2018.Regarding the proportion of the area under productive and sustainable agriculture(SDG 2.4.1),the global total cropland area has shown a steady growth trend,with an increase rate of 3.05 km2 per year from 2015 to 2022.The area of Chinas well-facilitated farmland increased from about 20%
35、of the total cropland area in 2015 to over 50%in 2022.The efficiency of resource utilization,including fertilizers,pesticides,irrigation water,and land,improved by 8.8%to 25.3%.The income of practitioners increased on average by 56.4%.Chinas terraced land area slightly increased.The existing terrace
36、d fields reduce the soil water erosion of cropland in China by about 50%.notably,the organic carbon content in Chinas cropland topsoil increased by 3.4%from 2015 to 2020.Regarding SDG 6(Clean Water and Sanitation),the report focuses on eight indicators of six targets and conducts global/China midter
37、m progress evaluations.The research findings reveal that since 2001,the transparency of 41.4%of large lakes and reservoirs worldwide(SDG 6.3.2)has shown a significant upward trend;the overall improvement rate in cropland water-use efficiency in agricultural areas worldwide has been 3.5%;the distribu
38、tion area of lakes and reservoirs has grown by 719.1 km2 per year globally.In China,significant progress has been made in achieving SDG 6.Since 2015,the surface water sources meeting the water quality safety standards increased by 3.5 percentage points.The ratio of public toilets per 10,000 urban re
39、sidents(SDG 6.2.1a)increased by 11.2%.The number and capacity of wastewater treatment plants(SDG 6.3.1)increased by 111.6%and 56.1%,respectively.26 provinces in China saw increases in the proportion of surface water bodies with good water quality(SDG 6.3.2).Chinas overall water stress level(SDG 6.4.
40、2)decreased from 66%to 58%.The area of natural and artificial water bodies(SDG 6.6.1)has been increasing,with reservoir water surface area growing by approximately 7%.From 2001 to 2019,water use efficiency of wheat,corn,and rice cultivation in China increased by 33.4%,20.0%,and 14.1%,respectively.Ch
41、inas evaluation score of integrated water resource management implementation(SDG 6.5.1)increased from 75 in 2017 to 79 in 2020.Regarding SDG 7(Affordable and Clean Energy),the report focuses on six indicators of five targets and conducts global/China midterm progress evaluations.The research finding
42、s indicate that China has achieved significant progress in all SDG 7 indicators.By 2020,the global electrified built-up areas(SDG 7.1.1)improved significantly compared to 2014,and China achieved complete access to electricity in 2015.Chinas population relying on clean cooking energy and technology(S
43、DG 7.1.2)reached 83.55%in 2022.Chinas clean energy transition has made significant progress.By 2022,Chinas wind power and photovoltaic installed Executive Summary06Big Earth Data in Support of the Sustainable Development Goals(2023)capacity increased by 2.8 and 9.2 times,respectively,compared to 201
44、5.Chinas renewable energy electricity transmitted via ultra-high voltage increased by 1.69 times.The energy consumption per unit of Gross Domestic Product in China(SDG 7.3.1)decreased by one-fifth from 2014.China has established a systematic framework for international cooperation in energy,in terms
45、 of energy policies,energy project,green energy utilization,and energy technology cooperation(SDG 7.a.1/SDG 7.b.1),contributing to global energy transition.Regarding SDG 11(Sustainable Cities and Communities),the report examines progress towards nine indicators under eight targets at the global and
46、Chinese scales.From 2015 to 2020,the proportion of the urban population with convenient access to public transport(SDG 11.2.1)increased by 3.4%globally.Urban land-use efficiency(SDG 11.3.1)increased worldwide.Heritage conservation(SDG 11.4.1)improved globally,and the number of affected people due to
47、 disasters(SDG 11.5.1)significantly decreased.Between 2015 and 2022,Particulate Matter 2.5(PM2.5)concentration(SDG 11.6.2)dropped from 21.6 g/m3 to 19.4 g/m3 globally.For China,considerable progress has been achieved in multiple SDG 11 indicators.The urban population in slum areas(SDG 11.1.1)decreas
48、ed by 30.8%;the proportion of the population with convenient access to public transport significantly increased;disaster-affected people and deaths(SDG 11.5.1)and direct economic losses(SDG 11.5.2)exhibited a clear downward trend;the exposure risk to PM2.5(SDG 11.6.2)decreased to 44.2%for Chinese re
49、sidents.Additionally,ecological greening efforts for urban construction land(SDG 11.7.1)have proved effective,and the central and eastern regions of China have outperformed the western and northeastern regions in terms of urban and rural regional development(SDG 11.a).Regarding SDG 13(Climate Action
50、),the report examines midterm progress towards four targets through seven indicators globally and in China.The research finds that progress has been made in disaster prevention and reduction both in China and the world,but significant challenges remain in reducing greenhouse gas emissions.From 2016
51、to 2021,the global annual number of disaster-affected people and deaths(SDG 13.1.1)decreased by 42.2%and 78.0%,respectively,compared to the period from 2000 to 2015.China also witnessed a significant reduction from 2016 to 2021 in the number of disaster-affected and deceased/missing people per 100,0
52、00 population by 57.7%and 64.8%,respectively,compared to the period from 2010 to 2015.China has developed comprehensive national disaster risk reduction policies(SDG 13.1.2)based on the Sendai Framework for Disaster Risk Reduction,and the proportion of local governments with disaster risk reduction
53、strategies(SDG 13.1.3)has reached 100%.Moreover,after 2020 China set targets for carbon emissions peak and carbon neutrality,forming a strategic framework to address climate change(SDG 13.2.1/13.b.1).However,global greenhouse gas emissions(SDG 13.2.2)has resumed an upward trend since 2021 after a te
54、mporary decrease in 2020,and similarly China faces considerable pressure on emissions control.Climate change education(SDG 13.3.1)in China is still at its early stages,although the introduction of the“dual carbon”goals has led to increased public awareness of climate change issues.Regarding SDG 14(L
55、ife Below Water),the report assesses Chinas midterm progress in four targets.notable progress was observed in all indicators.For instance,marine pollution reduction efforts(SDG 14.1)led to significant decreases in the concentrations of dissolved inorganic nitrogen,dissolved inorganic phosphorus,and
56、reactive silicate in the coastal waters of Eastern China from 2009 to 2019.In 2018,the abundance of floating debris in Chinas coastal waters decreased by approximately 25%compared to the average value from 2010 to 2014.From 2018 to 2021,the average abundance of microplastics in Chinas coastal waters
57、 was at a low-to-medium level.Moreover,efforts to protect marine ecosystems(SDG 14.2)resulted in a net increase of 16%in Chinas mangrove area from 2015 to 2020,an increase in the value of coastal wetlands in typhoon preparedness,and improved ecological health in typical coastal bays.In terms of prot
58、ecting coastal and marine areas(SDG 14.5),the pace of returning enclosure to the sea and wetlands in coastal China continued to increase,with significant growth observed from 2018 to 2020.Sustainable management of aquaculture(SDG 14.7)in China was evident,showing a decreasing trend in the area of co
59、astal aquaculture ponds from 2015 to 2020 and a shift towards raft aquaculture,which grew orderly in area from 2015 to 2021 with a trend of its distribution moving farther away from the coastline.07Executive SummaryRegarding SDG 15(Life on Land),the report examines midterm progress towards five targ
60、ets through six indicators globally and in China.China made significant advancements in all SDG 15 indicators.notably,Chinas forest cover(SDG 15.1.1)showed a clear increasing trend,with significant success in afforestation efforts.Since 2015,global land degradation(SDG 15.3.1)showed signs of improve
61、ment,and China achieved land degradation neutrality ahead of schedule.During the monitoring period(20152020),the average annual net restoration rate of land increased by nearly 5%compared to the baseline period(20002015).Regarding the protection of mountain ecosystems,by 2020,two-thirds of Chinas ke
62、y protected wild species and 86.9%of priority-protected natural ecosystems in mountain areas were covered by natural protected areas,providing significant safeguards for mountain biodiversity conservation(SDG 15.4.1).The Mountain Green Cover Index(SDG 15.4.2)was stable globally and in China between
63、2015 and 2020,and China has also achieved this indicator.Furthermore,efforts to protect threatened species,as indicated by the Red List Index for higher plants in China(SDG 15.5.1),showed a slight increase from 2013 to 2020,indicating stability and improvement in conservation efforts.Additionally,ef
64、forts to prevent and control invasive alien species(SDG 15.8.1),such as potato beetle among six typical pests,exhibited positive results from 2012 to 2020.Finally,the report also discusses SDGs integrated assessment and interactions,examining four themes:integrated evaluation of SDGs in 285 cities a
65、t or above the prefecture level of China,SDGs synergies and trade-offs,spatial spillover effects,and future scenario simulations.The report finds significant spatial differences in the comprehensive scores and balance scores of SDGs across Chinas cities at or above the prefecture level,with urban cl
66、usters outperforming non-cluster areas in SDGs development.The analysis also reveals that there are more synergies than trade-offs between SDGs among cities,with more intensive interactions observed in urban clusters such as the Yangtze River Delta,Chengdu-Chongqing,and the Pearl River Delta.The rep
67、ort points out the importance of urban cluster strategies in driving sustainable urban development,with a focus on key goals like SDG 4(Quality Education),SDG 11(Sustainable Cities and Communities),SDG 12(Responsible Consumption and Production)and SDG 15(Life on Land)to promote the overall achieveme
68、nt of the SDGs.08Big Earth Data in Support of the Sustainable Development Goals(2023)As we reach the midpoint in the implementation of the Transforming our world:the 2030 Agenda for Sustainable Development(referred to as the 2030 Agenda)in 2023,we need to assess,at this moment of reflection,the curr
69、ent progress on the SDGs with more accurate data.This work can help us to have a big-picture view of the issues facing us all globally as well as individual nations,and to find science-based solutions that will see us through the second half of the implementation process.According to the Sustainable
70、 Development Goals Report issued by the United nations that monitored progress on the 17 goals(Un,2022;Un,2023a)and our own assessment of more than 230 indicators(Figure 1-1),about half the countries of the world are severely lacking in progress data with respect to the indicators,timely updates and
71、 geospatial information.This lack of data severely hinders the ability of nations to monitor progress and to make informed decisions.The midterm review represents an opportunity to examine the progress and weaknesses in our implementation worldwide and also to enhance data acquisition.As of now,out
72、of the 230 plus indicators,35%exhibit a lack of data at Tier II(having methods but no data).Even for indicators where data is available,data distribution is extremely uneven across countries and sectors.Developing and underdeveloped nations,in particular,are hampered by the lack of large-scale data
73、computing capacity and adequate resources and are badly in need of reliable global data products.The United nations Secretary-Generals Roadmap for Digital Cooperation aims to promote digital technology in accelerating the process of sustainable development(Un,2020a).Big data is an important method a
74、nd output of digital technology.We have been using Big Earth Data,encompassing multi-source data such as satellite observations,station records,survey statistics,internet media,and basic geographic data,to make up for the lack of statistical data and at the same time to provide rich spatial-temporal
75、 information to show the spatial differences and progress in the indicators.The Sustainable Development Science Satellite 1(SDGSAT-1)launched in november 2021 is a scientific satellite dedicated to serving the 2030 Agenda.Currently,the satellite data have been shared globally to further enhance SDG
76、data acquisition capability.From 2019 to 2022,we released four reports on Big Earth Data in Support of the Sustainable Development Goals(SDG Reports,http:/ promote the achievement of these goals in China and the rest of the world.The 2023 Report continues to monitor progress on indicators at the Chi
77、nese and global scales from the perspectives of method,data and decision support.The focus of this years Report is on midterm progress assessment of indicators in China and globally.More than one third of the over 230 SDG indicators are environment-Introduction Figure 1-1 Numbers of countries/region
78、s having data on indicatorsBig Earth Data in Support of the SDG Midterm Evaluation09Introductionrelated(UnEP,2021a),where the temporal and spatial advantages of Big Earth Data are most evident.As research on Big Earth Data deepens,the data that can be used to calculate indicator implementation is al
79、so increasing and routinely being updated.Over the past five years we have mainly provided public data products at the global scale on multiple SDG indicators,such as on cropland(SDG 2),natural and artificial water bodies quality(SDG 6),electrification(SDG 7),urban impervious surface and urban publi
80、c space(SDG 11),greenhouse gas emissions and natural disaster impact(SDG 13),aquaculture and mangrove distribution(SDG 14),forest cover and land degradation(SDG 15),and data products from SDGSAT-1.These products are world-leading in resolution,timeliness,and accuracy and can be directly applied to t
81、he assessment of SDG implementation worldwide.At the same time,we have built the Big Earth Data sharing service platform and the online display platform for the sharing,display and online calculation of indicators.At the Chinese scale,based on the data sets from the 20192023 reports and national and
82、 United nations(Un)statistics as well,analysis was done on the progress on 98 mainly environmental indicators in China from 2010 to 2022(Figure 1-2).Some quantitative findings on the progress are exploratory results of applying critical Big Earth Data processing,analytics and other innovative method
83、s.The results show consistent and steady improvement on the indicators assessed during the period 20102022.Between 2010 and 2015,76.5%of those indicators improved continuously while 14.7%deteriorated.Between 2015 and 2022,the improvement trend was further strengthened,with 81%continuing to improve a
84、nd none deteriorating.In 2015,only 11 out of the 98 indicators were achieved,but by 2022,more than half(51 indicators accounting for 52%)were achieved,meeting the 2030 Agenda ahead of schedule.The data show that China has made great progress on environmental indicators since its implementation of th
85、e SDGs in 2015.Among the assessed indicators,more than 60%under the three GoalsSDG 2 Zero Hunger,SDG 13 Climate Action and SDG 15 Life on Land have already been achieved.Discussion on SDG IndicatorsGiven its advantage of quick update,repeatability and broad coverage,big data can play an important ro
86、le in closing the gap in national data and computing indicator-related data with global consistency(Guo,2019;2020;2021).However,based on our long-term indicator research and tracking,from the perspective of big data analysis and computing,we propose three improvements to the definition and evaluatio
87、n criteria of some indicators:(1)The data comparability standards for some indicators are not high.The data obtained according to some indicators definitions fail to reflect differences in population,level of development and geography between countries.For example,under SDG 13.2.2 annual total green
88、house gas emissions,the differences in the size of population and economy between countries are not considered.(2)The evaluation of environmental indicators requires more quantitative standards.Some criteria cannot be quantified,making it difficult to measure progress or achievement.For example,SDG
89、14.a.1,which is the proportion of total research budget allocated to research in the field of marine technology.It is hard to quantify the proportion at which the indicator is achieved and an increase in the proportion of marine research could lead to a decrease in the proportion of other research a
90、ctivities,such as in agriculture and climate change.(3)The spatial data of some indicators are difficult to obtain.Some indicators are defined in a way that distinguishes between men and women and persons with disabilities,such as SDG 11.2.1,which is the proportion of population that has convenient
91、access to public transport and SDG 11.7.1,which is the average share of the built-up area of cities that is open space for public use for all.They may intend to emphasize the protection of vulnerable groups,but the population grids with disaggregated data of men and women and persons with disabiliti
92、es in cities are difficult to obtain on a broad basis,and statistical data alone cannot reflect spatial differences in the calculation of indicators.In view of the current worldwide differences in countries data acquisition capacity and issues concerning the setting of indicators,we recommend the fo
93、llowing:(1)nations should strengthen support in the continued enhancement of their ability of using big data in indicator calculation.(2)the Un may improve the clarity of definitions and criteria of some of the indicators based on data availability considerations.The enhanced clarity could allow glo
94、bally consistent big data to play a more significant role and move us towards greater data equity and availability.10Big Earth Data in Support of the Sustainable Development Goals(2023)Figure 1-2 Chinese midterm progress evaluation on 98 SDG indicators based on Big Earth DataSDG SDG SDG SDG SDG.a.SD
95、G indicatorsAssessment content using Big Earth Data.SDG.b.a.SDG.a.b.b.b.SDG SDG SDG SDG.Material consumption per capitaProportion of food loss and wasteSubmission of information required by international agreementsProportion of population with access to electricityProportion of population relying on
96、 clean cooking fuels and technologiesShare of renewable energy in total energy consumptionDistribution of energyintensive industrial heat sourcesPer capita installed capacity of renewable energyMaterial footprint per capita Material consumption per capita Proportion of urban population living in slu
97、msProportion of population with convenient access to public transport Ratio of land use to population growth rateRegulations and legislation on public participation in urban planningPer capita expenditure on conservation,protection,and management of nature reservesAnnual variation in natural disaste
98、r losses at prefecturelevel cities Annual variation in natural disaster losses at prefecturelevel cities Proportion of solid waste treatedMonitoring and analysis of urban fine particulate matter(PM.)Proportion of open public space citiesImplementation of Sendai Framework for disaster risk reduction
99、Implementation of Sendai Framework for disaster risk reduction at the local levelSustainable consumption and policy transformationMaterial footprint per capitaProportion of population with secure land tenure rightsNumber of deaths,missing persons,and directly affected individuals per,population due
100、to disastersProportion of direct economic losses from disasters to GDPEstablishment of national disaster reduction strategiesEstablishment of provinciallevel government disaster reduction strategiesChanges in stunting among children under years of ageChanges in obesity rate among children under year
101、s of ageMonitoring of sustainable development progress in food productionPremature deaths from air pollution in ChinaSafety assessment of drinking water sourcesConservation of plant genetic resourcesProportion of local varieties classified as endangeredNumber of deaths per,population due to road tra
102、ffic injuriesEvaluation of safety monitoring for drinking water sourcesProportion of population with secure rights over landSpatiotemporal variations of wetlandsProportion of treated domestic and industrial wastewaterProportion of water bodies with good water qualityWater use efficiency and analysis
103、 of changesWater stress levelIntegrated water resource managementProportion of crossborder river basins with signed water cooperation agreementsAvailability of adequate sanitation facilitiesGovernment investment in sanitationDeaths from accidental poisoning per,populationSustainable development educ
104、ationDegree of community engagement in water and sanitation management through legal or policy proceduresCarbon emissions per unit of GDPRural population with limited access to road services and changesAnalysis of effectiveness of Chinasponsored solar energy utilization training programs11Introducti
105、onWorseningImprovingStableThe length of bars in dark indicates the years covered by data during the period of Status of Number of Indicators evaluatedClose or achievedChallengingVery challengingVery challengingChallengingClose or achieved SDG SDG SDG SDG SDG SDG SDG SDG SDG SDG SDG SDG SDG SDG SDG S
106、DG indicators.a.b.c.a.b.SDG SDG SDG.a.c.Assessment content using Big Earth Data.a.b.c.SDG SDG Proportion of hazardous waste treatedProportion of solid waste treatedNumber of companies publishing sustainability reportsLevel of implementation of sustainable public procurement policies and action plans
107、Climate change and sustainable development education and outreachInstalled renewable energy capacity(per capita Watts)in ChinaFossil fuel subsidies per unit of GDPNumber of implementations of Environmental and Economic Accounting Systems(SEEA)Annual variation in natural disaster losses at prefecture
108、 level citiesImplementation of Sendai Framework for disaster risk reduction in ChinaImplementation of Sendai Framework for disaster risk reduction at the local levelNational adaptation strategiesPer capita greenhouse gas emissions in ChinaClimate change and sustainable development education levelCli
109、mate change financingNational adaptation strategiesChanges in distribution of marine debris and microplastics in nearshore areasMangrove areaAcidification status of the Yangtze River estuary and adjacent watersFishery productionCoverage of protected areas in relation to marine areasProgress in imple
110、menting international instruments to combat illegal,unreported,and unregulated fishingProportion of GDP derived from marine industriesProportion of research funding allocated to ocean researchProgress in implementing the United Nations Convention on the Law of the SeaForest coverageProportion of imp
111、ortant sites for biodiversity covered by protected areasAssessment of land degradationAssessment of mountain biodiversity conservation statusHighresolution monitoring of mountain green cover indexRed List index for threatened speciesParties to the Nagoya ProtocolConfiscated/seized import and export
112、species in wildlife tradeAgricultural invasive alien speciesFormulation and implementation progress of biodiversity strategies and action plansInvestment in biodiversity conservationInvestment in biodiversity conservationConfiscated/seized import and export species in wildlife tradeExport value of h
113、armless environmental technologiesNet official development assistance and grants from official members of development assistance committees and multilateral organizationsSustainable Development Goal Progress ReportData acquisition capabilityConservation of natural and artificial forestsSustainable D
114、evelopment Goal policies and mechanisms12SDG 2 Big Earth Data in Support of the Sustainable Development Goals(2023)SDG 2Zero HungerSDG 2Background 13Midterm Progress 13Thematic Studies 15Recommendations and Outlook 2213SDG 2 SDG 2 Zero HungerAs we approach the midpoint of the 2030 Agenda,the world i
115、s facing challenges such as climate change,extreme weather events,political conflicts,economic shocks,and growing inequality,which have diverted us from the track of achieving SDG 2 Zero Hunger(Un,2022).Furthermore,projections indicate that by 2030,there will still be around 600 million people world
116、wide facing hunger,a level similar to that in 2015 when the 2030 Agenda was launched(FAO et al.,2023).Achieving the Zero Hunger goal requires a 28%increase in global average agricultural productivity over the next decade,which is more than three times the growth rate of the past decade(OECD and FAO,
117、2022).Sustainable land,soil,and water resource management form the foundation for ensuring nutrition,diversified diets,and resource-efficient value chains in the transition to sustainable consumption.About 90%of the calories and 80%of the protein consumed by humanity come from cropland(Kastner et al
118、.,2012).However,due to population growth,per capita cropland area decreased by about 18%from 2000 to 2020(FAO,2022).Additionally,evidence suggests that the growth rate of agricultural productivity is slowing down,and high pollution and emissions have pushed production capacity to its limits,leading
119、to land and environmental degradation(FAO,2021).The Food and Agriculture Organization of the United nations(FAO),being the custodian agency for nine of the 14 indicators under SDG 2 and the contributing agency for another indicator,has developed a comprehensive system for statistical survey data col
120、lection and sharing.This system provides valuable and abundant data and knowledge related to agriculture,natural resources,and food systems for scientists and stakeholders from different sectors and regions worldwide.It offers strong data support for promoting sustainable land,soil,and water resourc
121、e management and achieving Zero Hunger.However,there is significant disparity in statistical survey capabilities among countries,with nearly 50%of countries lacking data that can be used for assessing progress toward Zero Hunger.Currently,seeking innovative technology and data has become one of the
122、four key accelerators for implementing the 2030 Agenda and achieving FAOs Strategic Framework 20222031.Over the past four years,this report has focused on innovating and implementing a series of Big Earth Data-enabled monitoring methods for indicators of meeting nutritional needs and ensuring sustai
123、nable food production,exploring ways of future upgrading and laying a solid foundation for the midterm progress assessment of SDG 2.This year,we continue to explore the monitoring of sub-indicators related to land and soil under Tier II SDG 2.4.1.Simultaneously,we will review and assess Chinas polic
124、ies on sustainable land use for food production and evaluate their effectiveness.Based on these efforts,this chapter will conduct a global/China midterm progress assessment,providing scientific support to understand the global/China implementation process of SDG 2,identify issues and gaps,and improv
125、e and formulate acceleration strategies.It aims to provide a data foundation and experience for achieving SDG 2 in China and the rest of the world.Based on the reports from 2019 to 2022 and the findings of this chapter,the localized progress evaluation was conducted of the nutritional health status
126、of three indicators:prevalence of stunting among children(SDG 2.2.1),prevalence of overweight among children(SDG 2.2.2),and prevalence of anemia in women of reproductive age(SDG 2.2.3).The midterm progress of indicators such as proportion of agricultural area under productive and sustainable agricul
127、ture(SDG 2.4.1)globally and in China was assessed.The assessment based on Big Earth Data showed that although the global agricultural area has been increasing,the per capita cropland is continuously decreasing;on the basis of progress towards meeting nutritional needs,China is moving towards sustain
128、able food production.1.Regarding progress towards meeting nutritional needs(SDG 2.2.1,SDG 2.2.2 and SDG 2.2.3),the localized midterm progress study of China found that China has essentially achieved the expected results for these three indicators,including:BackgroundMidterm Progress14SDG 2 Big Earth
129、 Data in Support of the Sustainable Development Goals(2023)-The prevalence of stunting among children under six years of age(SDG 2.2.1)in China declined from 8.1%in 2013 to 4.8%in 2017.During the monitoring period,the prevalence rates in urban and rural areas decreased from 4.2%and 11.3%to 3.5%and 5
130、.8%respectively.The decline in rural areas was more significant,narrowing the urban-rural gap.-The prevalence of overweight among children under six years of age(SDG 2.2.2)in China showed a moderate decline,decreasing from 8.4%in 2013 to 6.8%in 2017.Specifically,the prevalence dropped from 8.4%to 6.
131、9%in urban areas,and from 8.4%to 6.7%in rural areas,indicating a slower reduction in urban areas.-The prevalence of anemia among women of reproductive age(SDG 2.2.3)in China also showed a moderate decline,decreasing from 15.0%in 2012 to 14.5%in 2018.2.Regarding sustainable food production(SDG 2.4.1)
132、,the global and China-scale monitoring of relevant sub-indicators was conducted in 2019,2022,and during this chapters research.It was found that global per capita cropland resources are becoming increasingly scarce.-The total global agricultural land area showed a steady growth trend,with an annual
133、increase of 5.26 km2 from 2000 to 2015,which slowed down to 3.05 km2 per year after 2015.-In China,since 2000,the environmental impact per unit production has gradually decreased,and overall,land use has moved towards greater sustainability.As a practical approach to developing productive and sustai
134、nable agriculture(SDG 2.4.1),China has been implementing well-facilitated farmland construction since 2011.The area of such cropland increased from approximately 20%of the total cropland area in 2015 to over 50%in 2022,laying a solid foundation for establishing sustainable agricultural production sy
135、stems.Evaluations of the well-facilitated farmland projects effectiveness showed that resource use efficiency,including fertilizers,pesticides,irrigation water,and land,increased between 8.8%to 24.3%,with an average increase in practitioners income of 56.4%.-In terms of the sub-indicator related to
136、soil under SDG 2.4.1,from 2015 to 2020,Chinas cropland topsoil organic carbon increased by 3.4%.Additionally,the cropland soil is projected to remain a carbon sink in the future,although the carbon sink intensity is decreasing.From 2015 to 2022,the area of terraced fields in China slightly increased
137、.The existing terraced fields reduce the total soil water erosion of cropland in China by about 50%.SDG 2 Zero Hunger:Global/China Midterm Progress15SDG 2 SDG 2 Zero HungerThematic StudiesBased on continuous time-series satellite observations,we established a coupled monitoring model to detect conti
138、nuous change and dynamical update of cropland,enabling global monitoring of cropland dynamics at 30 m resolution from 1985 to 2022.Concurrently,in conjunction with global population statistics,we analyzed the per capita cropland availability at the national scale,providing scientific knowledge and d
139、ata support for assessing food security in different countries.From 1985 to 2022,the total global cropland area showed a stable growth trend,with a reduced annual growth rate of 3.05 km2/a after 2015.Cropland is mainly concentrated in regions with flat terrain and relatively abundant rainfall,such a
140、s East Asia,South Asia,Europe,the north American Great Plains,and the La Plata Plain in southern South America(Figure 2-1).The increased cropland mainly occurred in Africa and South America,with South America experiencing extensive deforestation for cultivation,while Africa converting some previousl
141、y unused land to cropland,resulting in an increase of 883,700 km2 and 239,500 km2 in cropland,respectively(Figure 2-2).The increase in cropland in Europe,north America,Asia,and Oceania was not significant,with increments of approximately 88,400 km2,8,800 km2,122,600 km2,and 30,500 km2,respectively,s
142、howing a markedly lower growth rate compared to the other two continents.Among them,some croplands in Eastern Europe has experienced abandonment,and some Asian countries have seen cropland being converted to construction land.In terms of temporal changes,South America and Africa had the highest incr
143、ease in cropland,but their growth rates declined from 30,000 km2/a and 7,300 km2/a during 20002015 to 15,500 km2/a and 5,700 km2/a during 20152022,respectively.The per capita cropland availability on a global scale has shown a declining trend,with the rate of decrease slightly reducing from 2015 to
144、2022,averaging 0.88%annually.Combining this with population statistics,we analyzed the per capita cropland availability from 1985 to 2022.The results indicated that the global per capita cropland availability decreased from 3.83 km2 per 1,000 people in 1985 to 2.51 km2 per 1,000 people in 2022,with
145、an average annual decline of 0.93%.The rate of per capita cropland decrease slowed after 2015,reducing from 0.039 km2 per 1,000 people per year during 19852015 to 0.023 during 20152022.Asia,with a high population density,has quite limited land resources per capita,with countries like Japan,Banglades
146、h,India,and China having less than 2.00 km2 of cropland per 1,000 people.As the population continues to increase,per capita cropland availability in Asian countries is also showing a declining trend,with an annual decrease rate of 0.70%during 20152022.Cropland is a crucial resource for sustaining fo
147、od production and forms the foundation for agricultural development.However,the utilization of cropland also has environmental implications,such as increased soil erosion risk compared to natural vegetation,especially in the absence of proper agricultural management measures.In this theme,we first c
148、onducted global-scale monitoring of cropland changes.Subsequently,we focused on one of the sub-indicators of SDG 2.4.1soil erosion and degradation,and monitored the construction of terraced fields,a special soil conservation measure in China,and evaluated the effectiveness of terraced fields in miti
149、gating soil erosion.Through these monitoring efforts,we continue to enrich the technical methods and data from Big Earth Data for monitoring and assessing Tier II indicators of SDG 2 Zero Hunger.Global Monitoring and Evaluation of Cropland ChangesSDG 2.4:By 2030,ensure sustainable food production sy
150、stems and implement resilient agricultural practices that increase productivity and production,that help maintain ecosystems,that strengthen capacity for adaptation to climate change,extreme weather,drought,flooding and other disasters and that progressively improve land and soil qualityMonitoring o
151、f Sustainable Food Production Systems16SDG 2 Big Earth Data in Support of the Sustainable Development Goals(2023)17SDG 2 SDG 2 Zero HungerTerraced farming has been an important agricultural practice in China,given that approximately two-thirds of Chinas territory is covered by mountains(Wei et al.,2
152、016).Terraced fields not only ensure food production but also offer notable ecological benefits.In this section,we developed a new method based on remote sensing data for large-scale mapping of terraced field distribution in China.By utilizing remote sensing imagery and terrain data,we extracted mul
153、ti-temporal spectral and topographic features,constructed optimized feature sets,obtained samples through interpretation and sample migration techniques,and designed appropriate classification algorithms.Consequently,we generated two sets of 30 m spatial resolution data depicting the distribution of
154、 terraced fields in China for the years 2015 and 2022.Furthermore,we evaluated the soil conservation benefits of terraced fields in China through developing a large-scale terraced fields soil conservation benefit evaluation model based on the Revised Universal Soil Loss Equation(RUSLE),which allowed
155、 us to quantify the soil erosion reduction benefits of terraced fields in China.Terraced fields are widely distributed across China,accounting for about 1/4 of the national cropland area.From 2015 to 2022,about 46.7%of the newly added terraced fields were located in areas with slope gradients of 6 t
156、o 15.The spatial distribution of terraced fields in China is closely related to its topography,with primary concentrations in plateau mountainous regions(e.g.,the Loess Plateau,Distribution of Terraced Fields in Chinaand Their Soil Conservation BenefitsSDG 2.4:By 2030,ensure sustainable food product
157、ion systems and implement resilient agricultural practices that increase productivity and production,that help maintain ecosystems,that strengthen capacity for adaptation to climate change,extreme weather,drought,flooding and other disasters and that progressively improve land and soil quality Figur
158、e 2-4 Water erosion reduction benefits of terraced fields in areas with different slope gradientsNote:The pixel values in this figure represent the percentage of terraced fields within each 1 km 1 km grid cell,calculated based on the results of the 30 m resolution terraced field mapping.Figure 2-3 D
159、istribution map of terraced fields in China in 2022the Yungui Plateau),hilly regions(e.g.,the central and southeastern hills),and mountain-basin transition regions(e.g.,the eastern Sichuan Basin)(Figure 2-3).The provinces with higher proportions of newly added terraced fields from 2015 to 2022 were
160、mainly located in areas with numerous and dense pre-existing terraced fields,such as Gansu and Shaanxi on the Loess Plateau of northwestern China,and Sichuan and Yunnan in southwestern China.About 46.7%of the newly added terraced fields were located in areas with slope gradients of 6 to 15.The conve
161、rsion of sloping land to terraces in areas with high slope gradients plays a positive role in mitigating soil erosion resulting from improper land utilization.The soil conservation benefits of terraced fields in China are significant,and the existing terraced fields can reduce the total water erosio
162、n of cropland in China by about 50%.The benefits of terraced fields exhibit spatial heterogeneity.Terraced fields demonstrate particularly significant soil conservation benefits in agricultural regions dominated by mountainous and hilly landscapes.In southwestern and northwestern China,where both te
163、rraced field coverage and potential erosion rates are high,terraced fields contribute most to erosion reduction.In the eastern and southern hilly regions of China,terraced fields also show considerable 18SDG 2 Big Earth Data in Support of the Sustainable Development Goals(2023)erosion reduction bene
164、fits per unit terrace area.In regions with fewer terraced fields and lower potential erosion amount per unit terrace area,such as the northeastern region,the soil erosion reduction benefits of terraced fields are relatively low.The existing terraced fields can reduce the total water erosion of cropl
165、and in China by about 50%.In the areas with slope gradients ranging from 6 to 25,terraced fields lead to the greatest reduction in cropland erosion in terms of both total amount reduction and percentage reduction.In the areas with slope gradients greater than 25,terraced fields demonstrate the large
166、st erosion reduction per unit terrace area.The results indicates that terraced fields play a crucial role in soil conservation on steep-slope cropland.The method of literature analysis and historical analysis was employed to search,organize,and summarize the relevant policies in the field of croplan
167、d utilization and protection in China.The main content and development process of these policies were introduced,and a complete timeline of the history of cropland protection was created,showcasing Chinas experience in cropland protection and ensuring food security.From the historical evolution of c
168、ropland protection and utilization,China has gone through three important development stages after an initial period of experimentation:the stage of land development and utilization focusing on quantity,the stage of simultaneous emphasis on quantity and quality for cropland protection and utilizatio
169、n,and the stage of a new tripartite pattern,integrating quantity,quality,and ecology.In response to the issues in cropland protection,utilization and management,China continuously adjusted and improved specific policy measures,achieving comprehensive upgrades in the concept,system,measures,methods,a
170、nd entities of cropland protection and utilization.These upgrades include:1)Conceptual upgrade,from focusing on quantity to quantity+quality,and further to the tripartite management of quantity+quality+ecology;2)System upgrade,raising hierarchical levels from national policy to basic national policy
171、,then to lifeline,and finally red line;3)Measures upgrade,transitioning from simple protection and utilization to a comprehensive system of controls,construction and incentives for protection and utilization,;4)Methods upgrade,evolving from solely administrative measures to a combination of administ
172、rative,economic,and engineering measures,supported by a plethora of technical means;5)Entity upgrade,shifting from a single governing entity(the government)to a composite and diversified governing entity(including the government,society,and farmers),effectively promoting the integration Cropland is
173、the foundation for food production.Food security relies on safeguarding the productive capacity of cropland.China has implemented the strategy of ensuring food production through sustainable cropland use and innovative agricultural technology,forging a distinctive path for cropland utilization and p
174、rotection.With only 7%of the worlds cropland,China feeds about 20%of the global population,achieving remarkable accomplishments.This theme systematically examines Chinas policies and systems related to cropland protection and utilization.It also conducts a comprehensive assessment of the effectivene
175、ss of the well-facilitated farmland construction,aimed at productive and sustainable agriculture,showcasing Chinas approach to ensuring food security and promoting sustainable development.Chinas Cropland Utilization and Protection Policies and Experience SharingSDG 2.3:By 2030,double the agricultura
176、l productivity and incomes of small-scale food producers,in particular women,indigenous peoples,family farmers,pastoralists and fishers,including through secure and equal access to land,other productive resources and inputs,knowledge,financial services,markets and opportunities for value addition an
177、d non-farm employmentSDG 2.4:By 2030,ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production,that help maintain ecosystems,that strengthen capacity for adaptation to climate change,extreme weather,drought,flooding and other
178、disasters and that progressively improve land and soil qualityEvaluation of Sustainable Food Production Policies and Benefit 19SDG 2 SDG 2 Zero Hunger Figure 2-5 Statistical analysis of effectiveness of cropland utilization and protection and refinement of cropland utilization and protection.Overall
179、,the development stages of cropland protection and utilization in China reflect the countrys progression from extensive utilization to transformative protection and utilization,and further to high-quality protection and utilization.Land use survey,farmland grading,well-facilitated farmland construct
180、ion and other practical measures have been taken to implement cropland sustainable utilization and protection policies.Starting from 1984,China successively issued technical regulations such as Technical Guidelines for Land Use Survey,National Land Classification(Trial Implementation),Land Use Class
181、ification,Regulations for Farmland Grading and Regulations for Farmland Valuation,and conducted three large-scale surveys on land use at the national level,which provided information on the quantity,quality distribution,and dynamic changes of cropland.With the demarcation of three spaces and three l
182、ines and the formulation and implementation of national spatial planning,farmland preservation quantity and permanent basic farmland protection area and other targets were established as rigid indicators,and the responsibility assessment of cropland protection and food security is implemented.Relyin
183、g on the comprehensive monitoring and supervision platform for natural resources that covers the five layers of space,sky,ground,people,and network,a grid-based supervision system covering five levels,namely national,provincial,municipal,county,and township,was established to implement cropland prot
184、ection responsibilities in terms of area and location.In recent years,China has embarked on the construction of well-facilitated farmland,which refers to farmland that is equipped with modern infrastructure,technology,and management practices to enhance its productivity,sustainability,and efficiency
185、.This initiative covers eight aspects:land,soil,water,roads,forest,electricity,technology,and management,and corresponds to several sub-indicators of SDG 2.4.1,and is an essential measure for Chinas development of productive and sustainable agriculture.The area increased from approximately 20%of the
186、 total cropland area in 2015 to over 50%in 2022(Figure 2-5).The per mu(approximately 0.067 hectares)production of the well-facilitated farmland generally increased by 10%20%,providing significant support for the national grain output to stay above 6.51011 kilograms for years running.At present,well-
187、facilitated farmland projects are being documented and put under the digitized management.An information management platform for land consolidation is established to achieve the unification of land,data,and records,thus providing assurance for better construction and management of well-facilitated f
188、armland and for achieving sustainable agricultural development.20SDG 2 Big Earth Data in Support of the Sustainable Development Goals(2023)Well-facilitated farmland construction is an important measure adopted in recent years for sustainable utilization and protection of farmland in China.By the end
189、 of 2022,China had cumulatively completed the construction of one billion mu(6.67107 hectares)of well-facilitated farmland capable of producing stable high yield and resistant to drought and flood,ensuring a grain production capacity of over one trillion catties(approximately 51011 kilograms)and gua
190、ranteeing 80%of Chinas total grain output.In this case,a three-terminal metric method was constructed based on the spectral mixture decomposition model for substrate,vegetation and dark matter to monitor farmland production capacity.A Bayesian model was integrated to form a competitive learning mech
191、anism,focusing on typical areas implementing well-facilitated farmland projects,identifying changes in farmland production capacity and the timing of significant variations,and achieving dynamic monitoring of farmland production capacity before and after the construction of well-facilitated farmland
192、.Additionally,on-site investigations were carried out in key agricultural areas across the country to comprehensively assess the effects of well-facilitated farmland construction,contributing Chinese experience to the achievement of SDG 2.4.The monitoring of well-facilitated farmland productivity re
193、vealed that from 2010 to 2022,87%of well-facilitated farmland in the typical areas experienced increased productivity,with an average increase of 16%per mu.Farmland productivity monitoring based on ground observation data indicated that from 2010 to 2022,in the selected typical region(Jiangsu),87%of
194、 the well-facilitated farmland witnessed a significant increase in productivity(Figure 2-6).Despite frequent extreme weather events in recent years,most well-facilitated farmland in the region Evaluation of Chinas Cropland Construction BenefitsSDG 2.3:By 2030,double the agricultural productivity and
195、 incomes of small-scale food producers,in particular women,indigenous peoples,family farmers,pastoralists and fishers,including through secure and equal access to land,other productive resources and inputs,knowledge,financial services,markets and opportunities for value addition and non-farm employm
196、entSDG 2.4:By 2030,ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production,that help maintain ecosystems,that strengthen capacity for adaptation to climate change,extreme weather,drought,flooding and other disasters and that
197、 progressively improve land and soil quality Figure 2-6 Distribution characteristics of farmland production capacity changes in Jiangsu,a typical well-facilitated farmland region.(a)Distribution of significance of farmland production capacity changes in Jiangsu;(b)Improvement range of well-facilitat
198、ed farmland and comparison with non-well-facilitated farmland 21SDG 2 SDG 2 Zero Hunger Figure 2-7 Provincially assessed efficiency gains from well-facilitated farmland in Chinas key agricultural regions in saving land,water,fertilizer,pesticide and farmer income increase achieved five hundred kilog
199、rams for one season,and a ton of grain for two seasons,reflecting the improved disaster resilience of well-facilitated farmland.When excluding climate impacts,a comparison of productivity between well-facilitated farmland and non-well-facilitated farmland areas showed that 68%of well-facilitated far
200、mland patches exceeded the productivity of non-well-facilitated farmland,with an average increase of 16%per mu.For example,in Danyang,southern Jiangsu,578,900 mu(38,600 hectares)of well-facilitated farmland have been constructed,accounting for 75.4%of the total cropland area of the city,with grain p
201、roduction per mu increased by more than 80 catties(40 kilograms).Overall,the construction of well-facilitated farmland can effectively promote stable and increased grain production on cropland.After the implementation of the well-facilitated farmland projects,the efficiency of resource utilization,i
202、ncluding fertilizers,pesticides,irrigation water and land,increased by 8.8%24.3%.The income of practitioners significantly improved,with an average increase of 56.4%.The projects also led to a noticeable improvement in the scientific level of farming,resulting in a significant enhancement of compreh
203、ensive agricultural benefits.In the project areas,while reducing the cost of resource inputs,it also reduced agricultural non-point source pollution,saving land,water,fertilizer and pesticide per mu by 8.8%,24.3%,13.8%,and 19.1%,respectively,promoting low-consumption,high-efficiency,ecological,and s
204、afe sustainable agriculture.Furthermore,the implementation of the well-facilitated farmland projects greatly boosted the appropriate expansion of producing and operating entities.Each hectare of land could lead to an average increase of CnY 7,464 in farmers income,with an average increase rate of 56
205、.4%.77.3%of the project areas met the target of achieving an average increase of CnY 3,000 per hectare in farmers income(Figure 2-7).There are three main reasons for the increase in farmers income:first,during the construction of well-facilitated farmland,local farmers contributed their labor and ef
206、forts and received corresponding remuneration;second,after the construction of well-facilitated farmland,there is an increase in the crop value of the land,due to expanded sales channels as a result of improved transport convenience;third,the added benefits brought about by the well-facilitated farm
207、land construction are enjoyed by the locals through dividends obtained through land transfer or land shareholding,and at the same time,higher productivity releases more people from farming for higher-paying jobs.The comprehensive benefits of well-facilitated farmland construction show significant re
208、gional differences.In terms of land saving rate,the southwest region,characterized by fragmented and less leveled farmland,has shown significant improvement,with Guizhous land saving rate increasing by 18.5%,and Yunnan experiencing an increase of CnY 7,080 or 63.0%in land rental per hectare.The effe
209、ctiveness of fertilizer and pesticide saving is most pronounced in the southeast and southwest regions,where the terrain is more undulating and farmland is more fragmented,achieving gains of 18%24%.In comparison,the Yangtze River middle and lower reaches region has the lowest fertilizer saving rate
210、at 8.0%,and the northeast region has the lowest pesticide saving rate at 12.8%.Regarding water saving rate,the arid and semi-arid region of Inner Mongolia has achieved a water saving rate of 33.6%,with an average reduction of 1,621.5 m3 in irrigation water per hectare.In the southeast region,Guangdo
211、ng has reached a water saving rate of 45.9%.The southwest region stands out in terms of land saving rate and income increase rate,reaching 17.9%and 95.8%,respectively.Almost all project areas(96.2%)have seen an average increase in net farmer income per hectare of over CnY 3,000.22SDG 2 Big Earth Dat
212、a in Support of the Sustainable Development Goals(2023)Recommendations and OutlookThis chapter focuses on two main themes:the monitoring and evaluation of sustainable food production systems and the related policies and their benefits.It continues to innovate and explore the use of Big Earth Data-en
213、abled monitoring methods for SDG 2.4.1(proportion of agricultural area under productive and sustainable agriculture),where methods have been available but data was previously lacking.It also reviews and summarizes Chinas policies in promoting productive and sustainable agriculture and evaluates thei
214、r overall effectiveness.Based on this foundation,this chapter summarizes the results of the past four years case studies,and details a midterm evaluation.The findings show that China has been moving towards sustainable food production while gradually meeting nutritional needs.Based on this research,
215、we propose the following recommendations:1.Cropland is the foundation of agricultural development,and soil fertility preservation and enhancement are essential for ensuring food production.Terraced field,as a special type of cropland,plays a crucial role in soil protection in hilly and mountainous a
216、reas.In the future,there should be further reforms in the management system of cropland protection and utilization,with multiple stakeholders such as the central government,local governments,and ordinary farmers,implementing a comprehensive management system comprising monitoring,incentives,and regu
217、lation.Satellite remote sensing and drone technology should be utilized to establish a regularly updated monitoring system,incorporating various measures for soil protection into agricultural statistics and land spatial planning.Additionally,reward and incentive mechanisms primarily based on differe
218、nt types of subsidies for soil fertility preservation and enhancement should be trialled.2.Well-facilitated farmland construction,as an important measure in Chinas development of productive and sustainable agriculture,aims primarily to increase grain productivity but also to promote farmers income,r
219、esource efficiency,and emission reduction.It serves as a typical demonstration for achieving high-quality and sustainable agricultural development.Given that multiple factors can drive up cropland productivity,future planning should be based on regional characteristics,further strengthening the rati
220、onal distribution of well-facilitated farmland and setting synergistic objectives.Cooperation efforts should be intensified in improving soil quality and agricultural technological services to enhance the comprehensive benefits of synergistic objectives in cropland utilization.3.Looking ahead to the
221、 2025 comprehensive review,and drawing on the experience of continuous SDG 2 indicators monitoring,the following modification suggestions are proposed for the Zero Hunger indicators:Firstly,to better evaluate the achievement of Zero Hunger,food waste should be included as an indicator for SDG 2 just
222、 like food demands.Secondly,SDG 2.4.1 has always been in a state of lacking data despite having methods,due to certain extent to the fact that it is defined based on statistical surveys.Hence,appropriate adjustments should be made to the indicator definition to allow for monitoring using a wider ran
223、ge of data types.Thirdly,different countries/regions choose different age ranges when evaluating the nutritional health status of their residents.For example,when calculating the nutritional health status indicators such as the prevalence of stunting and the prevalence of overweight among Chinese ch
224、ildren,the age range is usually under six years,and the age range of the prevalence of anemia among Chinese women of reproductive age is usually 18-44 years old.It is suggested that when evaluating the nutritional health status of residents in different countries/regions,consideration be given to th
225、e differences in age selection among countries/regions as well as the consistency of evaluation indicators.Instead of defining specific age groups,relevant social activities can be used to distinguish them,such as children of preschool age and women of childbearing age,so as to enhance the actual co
226、mparability and availability of indicator data in different regions.In the future,we will continue to explore the capabilities of Big Earth Data in monitoring and evaluating food security and zero hunger,providing scientific evidence for pathways to achieve SDG 2.23SDG 2 SDG 2 Zero HungerSDG 6Backgr
227、ound 24Midterm Progress 25Thematic Studies 27Recommendations and Outlook 36Clean Water and SanitationSDG 6Multispectral remote sensing image of Taihu(SDGSAT-1,Feb 24,2022)24SDG 6 Big Earth Data in Support of the Sustainable Development Goals(2023)BackgroundAs we approach the halfway point of the 203
228、0 Agenda,fundamental changes have yet to be achieved in the development and utilization of water resources.Issues such as extensive and inefficient utilization,poor management,excessive extraction,and pollution of freshwater and groundwater resources are persisting.Globally,water-related ecosystems
229、are deteriorating at an alarming rate,and the progress towards the Clean Water and Sanitation Goal(SDG 6)is not on track as planned,a situation globally recognized(Un,2023b).The United nations Sustainable Development Goals Report 2022 revealed that due to a lack of monitoring,the quality of the wate
230、r resources relied upon by at least 3 billion people for survival remains unknown,and 730 million people are living in countries facing severe water scarcity.At the current pace,by 2030,an estimated 1.6 billion people,2.8 billion people,and 1.9 billion people will still lack access to safe drinking
231、water,sanitation facility,and basic handwashing facility,respectively.Progress needs to quadruple in implementing the indicators of safe drinking water,sanitation,and hygiene facilities if they are to be achieved by 2030(Un,2022).While global progress towards SDG 6 is assessed based on national stat
232、istics,it is insufficient to support policy-making and decision-making at various levels of government below the national level.In recent years,non-traditional data sources such as satellite remote sensing data,mobile phone data,and crowdsourced data have been providing valuable supplements to tradi
233、tional statistical data.For instance,the rapid development of Big Earth Data technology has significantly improved the monitoring and evaluation capabilities for SDG 6.These technological means enable high spatiotemporal resolution monitoring of relevant indicators through remote sensing,regular rev
234、isits,and rapid information extraction,leading to more accurate and objective assessment results while saving costs and time(Lu et al.,2021).Over the past four years,the report has conducted a series of case studies on global,regional,Chinese,and provincial scales to monitor and evaluate the progres
235、s on SDG 6.These research findings have laid a solid foundation for conducting midterm progress reviews at different scales.This year,we will combine these achievements to carry out midterm progress reviews at the global and Chinese scales.The aim is to provide scientific support for understanding t
236、he progress of SDG 6 implementation at these two levels,identifying issues and gaps,and providing scientific evidence for making improvements and acceleration strategies.The evaluations will also contribute data basis and experiential references to achieving SDG 6 at the global and Chinese scales.25
237、SDG 6 SDG 6 Clean Water and SanitationMidterm ProgressBased on the two reports of 2021 and 2022 and the research findings of this report,an understanding of the midterm progress has been formed concerning global-scale improvements in water environment,enhanced water use efficiency,changes in aquatic
238、 ecosystems,and China-scale achievements in safe drinking water,sanitation facilities,improved water environment,enhanced water use efficiency,integrated water resources management,and changes in aquatic ecosystems.The results show that from 2000 to 2022,the transparency of large lakes and reservoir
239、s showed a clear overall increasing trend,the water-use efficiency in cropland significantly increased,and the distribution range of lakes and reservoirs expanded.From 2015 to 2021,China made notable progress towards SDG 6,with SDG 6.1.1 and SDG 6.3.1 having been realized,while other indicators stil
240、l face varying degrees of challenges.1.In terms of safe drinking water(SDG 6.1),Chinas capacity to provide safe drinking water(SDG 6.1.1)has significantly improved.From 2015 to 2021,the surface water sources meeting the water quality safety standards in China increased by 3.5 percentage points,with
241、96.1%of the surface water sources meeting the safety standards in 2021.2.Regarding sanitation and hygiene(SDG 6.2),the condition of sanitation facilities in China(SDG 6.2.1a)has significantly improved.From 2015 to 2020,the growth rate of the number of public toilets per 10,000 urban residents was 11
242、.2%.3.In terms of improving the water environment(SDG 6.3),the reports of 2021 and 2022,as well as the research of this report,show significant improvements in Chinas surface water and groundwater environments,and stability in overall quality of groundwater.From 2015 to 2020,the number of sewage tre
243、atment plants in China increased from 4,300 to 9,100,with a growth rate of 111.6%.The sewage treatment capacity(SDG 6.3.1)increased from about 171 million tons/day to about 267 million tons/day,with a growth rate of 56.1%.From 2015 to 2020,26 provinces in China saw an increase in the proportion of s
244、urface water bodies with good water quality(SDG 6.3.2).From 2001 to 2022,41.4%of the large lakes and reservoirs globally showed a significant increase in transparency,while only 11.3%showed a significant decrease in transparency.4.In terms of improving water-use efficiency(SDG 6.4),the 2021 and 2022
245、 reports,as well as the research in this report,show that from 2001 to 2020,there was a clear upward trend in the water-use efficiency of cropland in agricultural areas worldwide,with an increase of 3.5%.From 2001 to 2019,Chinas agricultural water-use efficiency(SDG 6.4.1)significantly improved,with
246、 wheat,corn,and rice water-use efficiency(crop yield/evapotranspiration)increasing by 33.4%,20.0%,and 14.1%,respectively.From 2015 to 2020,Chinas overall water stress level(SDG 6.4.2)showed a declining trend,decreasing from 66%to 58%,indicating a moderate level of water stress.5.In terms of integrat
247、ed water resources management(SDG 6.5),the 2021 report showed that China had made remarkable progress in improving the level of integrated water resources management,with the comprehensive evaluation score for SDG 6.5.1 increasing from 75 points in 2017 to 79 points in 2020,reaching the medium-high
248、level globally.6.Regarding the protection and restoration of water-related ecosystems(SDG 6.6),the 2021 and 2022 reports,as well as the research in this report,show that from 2001 to 2021,the water surface area of lakes and reservoirs increased at a rate of 719.1 km2/a globally.From 2015 to 2020,bot
249、h natural and artificial water areas in China showed an increasing trend,with the water surface area of reservoirs increasing by approximately 7%.Compared to the period from 2005 to 2014,the rate of decline in Chinas groundwater storage from 2015 to 2020 slowed down by 65%.Compared to the period fro
250、m 2010 to 2015,the rate of loss of marsh wetlands in China from 2015 to 2020 slowed significantly,down from 4.1%to 0.8%.26SDG 6 Big Earth Data in Support of the Sustainable Development Goals(2023)SDG 6 Clean Water and Sanitation:Global/China Midterm Progress27SDG 6 SDG 6 Clean Water and SanitationTh
251、ematic StudiesSafe drinking water and sanitation facilities are directly related to the life and health of the general population.The Healthy China 2030 Blueprint promulgated in 2016,clearly stated the need to bring drinking water sources up to safety standards and strengthen groundwater management
252、and protection,which has effectively promoted the improvement of Chinas drinking water safety.In recent years,China has made significant progress in public health services through the implementation of the Toilet Revolution,with a substantial increase in the number of public toilets and a significan
253、t expansion in coverage to serve a wider population.Safe Drinking Water and SanitationThe compliance rate of Chinese centralized urban drinking water surface water sources at or above the prefecture level was 92.6%in 2015 and 96.1%in 2021,with an increase of 3.5 percentage points.In this case,the co
254、mprehensive compliance rates of surface water sources for drinking water in the 31 provinces(autonomous regions,municipalities directly under the central government,excluding Hong Kong,Macau,and Taiwan)were calculated using the Spearmans rank correlation coefficient,based on the location of automati
255、c monitoring stations and real-time online water quality data for drinking water sources in China in 2015 and 2021,and data from the Report on the State of the Environment in China 2015 and Report on the State of the Ecology and Environment in China 2021.The growth rates of each province from 2015 t
256、o 2021 were evaluated,where the growth rate=(2021 indicator value-2015 indicator value)/2015 indicator value 100%.In 2021,28 provinces(autonomous regions,municipalities directly under the central government)in China achieved a compliance rate of more than 90%in the water quality safety of surface dr
257、inking water sources.Compared to 2015,compliance rate in the water quality safety of surface drinking water sources for each province(autonomous regions,municipalities directly under the central government)in 2021 has significantly improved.Among them,Inner Mongolia had the highest growth rate in co
258、mpliance,indicating the most significant improvement in water quality of its drinking water sources,followed by Shandong and Zhejiang(Figure 3-1).The rapid increase in compliance rate is attributed to a series of water environment governance and protection policies,such as the Water Pollution Preven
259、tion and Control Action Plan formulated and implemented by the Chinese government since 2015.Assessment of Water Quality Monitoring in Chinas Drinking Water SourcesSDG 6.1:By 2030,achieve universal and equitable access to safe and affordable drinking water for all Figure 3-1 Spatial distribution map
260、 of compliance rate in water quality safety of surface drinking water source in China,2015202128SDG 6 Big Earth Data in Support of the Sustainable Development Goals(2023)Based on data from the China Statistical Yearbook and the China Urban and Rural Construction Statistical Yearbook,an analysis was
261、conducted on the change in the number of urban public toilets and the number of public toilets per 10,000 people in China for the years 2015 and 2020.Seven typical cities were selected from the seven major administrative regions of China.Utilizing data on land use,Digital Elevation Model(DEM),Points
262、 of Interest(POI),high-resolution population distribution,etc.,a monitoring model was developed to assess the proportion of the population covered by public sanitation facilities in these cities.The model calculated the proportions in these seven cities for the years 2015 and 2020,and further valida
263、ted the statistical results of public sanitation facility services.Monitoring the Proportion of Population Covered by Public Sanitation Facilities in ChinaSDG 6.2:By 2030,achieve access to adequate and equitable sanitation and hygiene for all and end open defecation,paying special attention to the n
264、eeds of women and girls and those in vulnerable situations Figure 3-2 Change rates in the total number of urban public toilets and the number of public toilets per 10,000 urban residents in China,20152020.(a)Change rates in total number of urban public toilets;(b)Change rates in number of public toi
265、lets per 10,000 people Figure 3-3 Spatial distribution of public toilet accessibility for urban residents in typical Chinese cities,20152020From 2015 to 2020,the number of urban public toilets in China increased by 22.1%,and the number of public toilets per 10,000 people increased by 11.2%.In 2015 a
266、nd 2020,the number of urban public toilets in China was 324,949 and 396,617,respectively,and the number of public toilets per 10,000 people was 4.0 and 4.5,respectively.In terms of changes,from 2015 to 2020,the number of public toilets and the number of public toilets per 10,000 people decreased to
267、some extent in the northeastern region,some provinces in the eastern coastal region,and ningxia.However,the other provinces showed varying degrees of increase.Among them,Xizang had the highest growth rates in the number of public toilets and the proportion of the population covered,reaching 129.7%an
268、d 71.6%,respectively(Figure 3-2).In 2015 and 2020,the proportion of the population covered by public toilets in seven typical cities in China increased from 19.0%to 28.7%.According to POI data,the total number of public toilets in these seven cities increased from 10,472 to 27,686 from 2015 to 2020,
269、with varying degrees of improvement in toilet accessibility for the population in all seven cities(Figure 3-3).Among them,Qinhuangdao showed the highest increase in public toilet accessibility for the population,reaching 781.8%.These monitoring results reflect the significant achievements of the Toi
270、let Revolution implemented in China in recent years.29SDG 6 SDG 6 Clean Water and SanitationCurrently,satellite remote sensing data is becoming the most important and cost-effective source of data for surface water quality monitoring.While filling data gaps,its advantages of large-scale and long-ter
271、m dynamic monitoring provide an effective approach to achieving global surface water quality monitoring and tracing long-term spatiotemporal changes.Improving Water EnvironmentUsing the 500 m resolution Moderate Resolution Imaging Spectroradiometer(MODIS)surface reflectance data(MOD09A1)for the peri
272、od 20002022,as well as measured transparency data set of Chinese surface water bodies,data sets from the Chinese national Earth System Science Data Center and Chinese Lake Science Database,European Multi Lake Survey(EMLS)shared data set,and AquaSat shared data set from the United States,also using t
273、he transparency inversion model of surface water bodies,based on the Forel-Ule Index(FUI)and hue angle (Wang et al.,2020),and the MODIS surface reflectance data for the summer season in both the northern and southern hemispheres,we constructed a data set of transparency for 1,117 large lakes and res
274、ervoirs worldwide with areas greater than 25 km2.Based on this data set,we analyzed the spatiotemporal trends of transparency in these large lakes and reservoirs.The overall transparency of large lakes and reservoirs globally follows a concave distribution with latitude.Lakes and reservoirs in high-
275、latitude regions near the poles have higher transparency,with an average transparency of around 4 m,while those in low-latitude regions around the equator and up to latitudes of 20 have lower transparency,with an average transparency of less than 1 m.Looking at the average transparency of large lake
276、s and reservoirs across different continents,those in Asia and Europe have higher transparency,while those in Africa have the lowest transparency.In terms of average transparency across different climatic zones globally,lakes and reservoirs in polar and cold temperate regions have higher transparenc
277、y,while those in tropical regions have lower transparency(Figure 3-4).Since 2000,there has been a clear overall upward trend in the transparency of large lakes and reservoirs worldwide.Approximately 41.4%of large lakes and reservoirs show a significant increase in transparency(p0.05),while only 11.3
278、%exhibit a significant decrease in transparency(p0.05)(Figure 3-5).Looking at the statistics from each continent,the average annual change rate of lake and reservoir transparency is positive for all six continents.Among them,the average transparency changes in Asia and Africa are relatively modest,w
279、ith an average change rate of 1.3 cm/a.In contrast,Europe demonstrates a significant increase in transparency,with an average change rate of 7.6 cm/a.Spatiotemporal Change in Transparency of Global Large Lakes and ReservoirsSDG 6.3:By 2030,improve water quality by reducing pollution,eliminating dump
280、ing and minimizing release of hazardous chemicals and materials,halving the proportion of untreated wastewater and substantially increasing recycling and safe reuse globally Figure 3-4 A comparison of the average transparency and the number of large lakes/reservoirs in different continents and clima
281、te zones from 2000 to 202230SDG 6 Big Earth Data in Support of the Sustainable Development Goals(2023)Improving water-use efficiency across various industries has always been a topic of great concern,closely related to human well-being and Sustainable Development Goals.Agriculture consumes a large a
282、mount of water,especially for evapotranspiration,making it crucial to enhance agricultural water-use efficiency as an important measure for achieving sustainable development and utilization of water resources.Improving Water-Use EfficiencyIn this report,cropland water-use efficiency was defined as t
283、he ratio of net Primary Productivity(nPP)to water consumed by evapotranspiration of cropland.Based on the global 1 km resolution annual dataset of cropland water-use efficiency from 2001 to 2020,we analyzed the spatiotemporal changes in global cropland water-use efficiency in terms of distribution a
284、nd interannual variation,and assessed the changes and improvements in cropland water-use efficiency at the global,regional,and country scales.From 2001 to 2020,there was a significant increasing trend in cropland water-use efficiency in agricultural areas worldwide,with an overall rise of 3.5%.Howev
285、er,spatial differences were observed.At the continental scale,differences were evident.Asia had the largest increase in cropland water-use efficiency(8.9%),followed by Oceania(7.2%).north America also showed an increasing trend,but with lower rates(4.6%)compared to Asia and Oceania.Europe,Africa,and
286、 South America had relatively small changes in cropland water-use efficiency(all less than 1%)(Figure 3-6).From 2001 to 2020,the cropland water-use efficiency of the worlds major grain-producing countries showed an upward trend.India,with a relatively low average cropland water-use efficiency,had th
287、e highest increase at 19.8%,followed by Canada at 18.2%,and China at 13.3%.Indonesia,Brazil,and France,with higher average cropland water-use efficiency,had smaller increases,all below 2%.The United States and Argentina had increases of 3.2%and 2.6%,respectively(Figure 3-7).Global Cropland Water-Use
288、 Efficiency ChangesSDG 6.4:By 2030,substantially increase water-use efficiency across all sectors and ensure sustainable withdrawals and supply of freshwater to address water scarcity and substantially reduce the number of people suffering from water scarcity Figure 3-5 Distribution of large lake an
289、d reservoir transparency change rates worldwide from 2000 to 202231SDG 6 SDG 6 Clean Water and Sanitation32SDG 6 Big Earth Data in Support of the Sustainable Development Goals(2023)The significant improvement in China and India is attributed to the much larger increase in cropland nPP(both over 25%)
290、compared to the increase in evapotranspiration(approximately 10%).This improvement is mainly due to factors such as advances in agricultural technology(e.g.,field management,water-saving measures,fertilization,breeding,etc.),adjustments in cropping structure and intensity,and climate change(e.g.,ele
291、vated carbon dioxide concentration,etc.)(Chen et al.,2019;Yang et al.,2022;Zhai et al.,2021).Figure 3-7 Annual variation of cropland water-use efficiency from 2001 to 2020 in the major grain-producing countries worldwide33SDG 6 SDG 6 Clean Water and SanitationAccording to the latest assessment repor
292、t by the United nations(Un,2022),water-related ecosystems worldwide are degrading at an alarming rate.In the past five years,nearly one-fifth of the global river basins have experienced significant changes in surface water area,including the addition of new water bodies due to floods and reservoir c
293、onstruction,as well as the disappearance of lakes,wetlands,and floodplains due to drought(UnEP,2021b).Satellite remote sensing technology enables precise monitoring and quantification of global and regional surface and groundwater dynamics.Changes in Water EcosystemsUsing the Global Surface Water(GS
294、W)data set from the European Commissions Joint Research Centre(JRC)(Pekel et al.,2016)as the data source,a spatial statistical overlay technique was employed to obtain global-scale water occurrence(referring to the proportion of observations classified as water to the total number of valid observati
295、ons during a given period,reflecting the frequency of water presence over the entire historical period)maps for each three-year period from 2001 to 2021.The maps were then overlaid with the Global Lakes(GLAKES)data set(Pi et al.,2022)and different global-scale reservoir data sets(Donchyts et al.,202
296、2;Wang et al.,2021)for analysis to construct a time-series data set of lake and reservoir areas weighted by water occurrence at different time periods.Based on this data set,the trends and spatial variations in natural lake and reservoir surface water area changes were analyzed.From 2001 to 2021,the
297、 global coverage of natural lake and reservoirs showed an overall expansion trend,with an area change rate of 719.1 km2/a.Among them,reservoirs exhibited continuous and significant expansion,with a change rate of 1133.5 km2/a.On the other hand,natural lakes showed a trend of initial shrinkage,follow
298、ed by expansion,and then shrinkage again,with a slight overall decline,and a change rate of-414.4 km2/a(Figure 3-8).At the continental scale,except for South America,the global reservoir surface water area has shown an Surface Water Area Changes of Global Natural Lakes and Reservoirs SDG 6.6:By 2020
299、,protect and restore water-related ecosystems,including mountains,forests,wetlands,rivers,aquifers,and lakes34SDG 6 Big Earth Data in Support of the Sustainable Development Goals(2023)Figure 3-8 Spatial-temporal pattern of surface water area change trend for global natural lakes and reservoirs over
300、the 20012021 period(1 1).(a)Global spatial-temporal pattern of changes in reservoir water area;(b)Global spatial-temporal pattern of changes in natural lake water areaNote:The gray color indicates that the inter-annual change rate of water body extent in that grid is not statistically significant.in
301、creasing trend in the past 20 years,with Asia(835.4 km2/a)and Africa(187.4 km2/a)exhibiting significant expansion.At the national scale,the reservoir surface water area has significantly increased in 46 countries,while only seven countries show a significant decreasing trend.The primary countries an
302、d regions displaying expansion trends include China,Russia,Southeast Asian(Vietnam,Laos,Cambodia,Myanmar,Malaysia),India,Pakistan,Iran,Trkiye,northeast Africa(Sudan,Ethiopia),and Canada.On the other hand,the countries and regions with shrinking trends are mainly concentrated in Brazil,Argentina,Thai
303、land,Iraq,Ukraine,and Southern Africa(South Africa,Zambia,Zimbabwe).Regarding natural lakes,no significant interannual changes have been observed on a continental scale.However,at the national level,the lake surface water area has significantly expanded in 68 countries,while 12 countries show a sign
304、ificant shrinking trend.Countries or regions with expansion trends include China,Southeast and South Asian(India,Pakistan,Myanmar,Indonesia),Iraq,Germany,the central and northwest part of Africa(Mali,nigeria,Democratic Republic of the Congo,Ethiopia,Kenya,etc.),the western coast of South America(Chi
305、le,Ecuador),and Greenland.On the other hand,countries or regions with shrinking trends are primarily located in Central and Western Asia(Kazakhstan,Uzbekistan,Turkmenistan,Iran,Afghanistan),Ukraine,Southeast Africa(Madagascar,Mozambique,Malawi),Australia,the United States,Canada,and central and sout
306、hern South America(Argentina,Bolivia).35SDG 6 SDG 6 Clean Water and SanitationThis study utilized spherical harmonic coefficients data from the Gravity Recovery and Climate Experiment(GRACE)satellites(20032017)and GRACE Follow-On satellites(20182022),Global Land Data Assimilation System(GLDAS)and Ca
307、tchment Land Surface Model(CLSM)v2.2 simulation data,and GLobal HYdrogeology MaPS(GLHYMPS)v2.0.The researchers employed a coordinated forward model that fused both satellite and model data(Pan et al.,2017)to calculate the groundwater storage changes in Africa at monthly and 0.5 resolution,represente
308、d in terms of equivalent water height.Furthermore,the study combined this data set with precipitation data from the Global Precipitation Climatology Centre(GPCC)to analyze the patterns of groundwater storage changes in Africa and the influencing factors.From 2003 to 2022,significant(p 80%),while val
309、ues were relatively low in Africa and South Asia(35 g/m3);(b)annual average population-weighted PM2.5 concentration in China and Beijing-Tianjin-Hebei region58SDG 11 Big Earth Data in Support of the Sustainable Development Goals(2023)Urban Assessment and Sustainable Development GoalsTo address the d
310、eficiencies in indicator data and Comprehensive Index results for the localization of SDG 11,and the lack of clarity in assessment methods at the city level,this case aims to supplement the relevant data from two perspectives:1.Based on the“check-ups”for pilot cities,the SDG 11 indicator system is l
311、ocalized,and data from pilot cities are collected,using the latest data collection methods.2.By drawing on existing United nations research,a Comprehensive Index is developed by fitting multiple indices,providing Chinese urban managers with quantitative analysis results for comprehensive decision-ma
312、king.It also serves as a tool for monitoring the process of achieving the SDGs by 2030.Data,collected from 59 pilot cities that had“check-ups”in 2022,went through SDG 11:Make cities and human settlements inclusive,safe,resilient and sustainable1 The term diamond structure is used in Chinas urban res
313、earch field to refer to a diamond-shaped distribution formed by four economic and social development growth poles of national importance:the Pearl River Delta,the Yangtze River Delta,the Beijing-Tianjin-Hebei region,and the Chengdu-Chongqing region.SDG 11 Comprehensive AssessmentThe process of local
314、izing SDG 11 in global cities still faces many challenges.At the international level,all cities encounter issues related to data collection,processing,and management(Fox et al.,2022).These challenges are manifested in several aspects:Firstly,due to differences among countries and regions,and in data
315、 availability,SDG indicators may not be applicable to specific local circumstances(Greene et al.,2017).Secondly,local governments lack the institutions and capabilities for data collection.For instance,many city-level governments lack the data monitoring capacity possessed by national-level governme
316、nts,further exacerbating the problem of mismatched data scales(Barnett et al.,2016).Lastly,the flow of resources,people and information that sustains city development crosses the boundaries of local political jurisdictions,making the measurement of urban sustainability progress a uniquely complex ch
317、allenge(Da Cruz et al.,2019;Fox et al.,2019).populated urban clusters where the risk had significantly decreased.The changes in annual average PM2.5 concentrations,weighted by population,are noticeable,with a national decline rate of 3.1 g/(m3a),representing a reduction of 36.5%.In the Beijing-Tianj
318、in-Hebei region,the decline rate and reduction reached 6.0 g/(m3a)and 50.7%,respectively.The gap in population-weighted PM2.5 concentrations between the Beijing-Tianjin-Hebei region and the entire country has been gradually narrowing(Figure 5-6).Additionally,by calculating the proportion of the popu
319、lation exposed to high PM2.5 concentrations(annual average 35 g/m3),the results show that by 2022,the national proportion decreased to 44.2%,while the proportion in the Beijing-Tianjin-Hebei region decreased to 66.5%.These findings reflect the substantial reduction in PM2.5 pollution levels in China
320、 in recent years,achieved through comprehensive air pollution control actions,leading to significant improvements in the health of Chinese residents and benefiting a wider population.Figure 5-7 Spatial distribution of SDG 11 localization comprehensive index results in 202259SDG 11 SDG 11 Sustainable
321、 Cities and Communitiesspatial analysis and generated results of the localized Comprehensive Index for SDG 11,as shown in Figure 5-7.The spatial pattern of the urban Comprehensive Index exhibits a high similarity to the diamond structure1 of urban economic and social development.Core cities in well-
322、developed urban clusters,such as the Pearl River Delta,Yangtze River Delta,Beijing-Tianjin-Hebei,Chengdu-Chongqing,the middle reaches of the Yangtze River,and the western coast of the Taiwan Strait,have higher levels of development.At the city level,Fuzhou,Shanghai,Guangzhou,Shenzhen,and nanjing hav
323、e the highest scores,reflecting their efforts and achievements in building inclusive,safe,resilient,and sustainable cities and human settlements.At the regional level,Central and Eastern China have the highest average scores,followed by South and Southwest China,while north China,northwest China,and
324、 northeast China have the lowest scores,indicating distinct differences between the eastern and western,and northern and southern regions.The seven major geographical regions,rated by the average scores of the Comprehensive Index,from high to low,are as follows:Central China,East China,South China,S
325、outhwest China,north China,northwest China,and northeast China.Overall,cities in the central and eastern regions perform better than those in the western and northeastern regions(Figure 5-8).Specifically,as shown in Figure 5-9,all regions have achieved relatively high levels in ensuring housing and
326、basic services(SDG 11.1),building transport systems(SDG 11.2),reducing negative environmental impacts(SDG 11.6),and providing public spaces(SDG 11.7).Recent assessments have consistently demonstrated significant progress in the hardware infrastructure development of Chinese cities.Figure 5-8 Bar cha
327、rt of the average scores of SDG 11 localization comprehensive index by geographical regions in 2022 Figure 5-9 Radar chart of average sub-index scores for SDG 11 localization by geographical regions in 202260SDG 11 Big Earth Data in Support of the Sustainable Development Goals(2023)Recommendations a
328、nd OutlookIn this chapter,we focused on the theme of sustainable cities and communities,conducting research on midterm progress in safe housing,urban public transport,urbanization,heritage conservation,urban disasters and responses,urban air pollution,open public spaces in cities,and urban-rural reg
329、ional development.Based on these studies and the outcomes of the past five years case studies,we summarized the global,regional,and Chinese progress towards SDG 11.The research findings reveal that the proportion of people with convenient access to urban public transport has increased in major globa
330、l cities,global heritage conservation is showing positive development,and the sources of typical urban atmospheric particulate matter are influenced by multiple factors.China is very close to realizing the indicator on the proportion of people with convenient access to urban public transport,and the
331、 risk of exposure to PM2.5 for Chinese residents is gradually decreasing.The localized Composite Index for SDG 11 shows differentiated characteristics,and Chinese cities have made significant progress in infrastructure construction.These research results will provide vital support for fine-grained a
332、nalysis of progress in different regions and for scientific decision-making on sustainable cities and communities.Based on the research in this chapter,we propose the following recommendations:1.To increase the proportion of people with convenient access to urban public transport and address the iss
333、ue of uneven transport convenience globally,efforts should be made to strengthen the construction and improvement of urban public transport systems and promote the integration of public transport planning with urban planning.2.Data completeness and accuracy are crucial for heritage conservation work.Timely updates of the World Database on Protected Areas and improvement of information on the UnESC