Dieses Kapitel geht auf die dringende Notwendigkeit von Klimaschutzmaßnahmen ein und beleuchtet die steigenden globalen Treibhausgaskonzentrationen und ihre verheerenden Auswirkungen auf Wettermuster und Biodiversität. Er präsentiert zwei zentrale Fallstudien: Die erste konzentriert sich auf die Entwicklung eines nahezu Echtzeit-Datensatzes für globale Kohlenstoffemissionen, der von 2019 bis 2022 tägliche Kohlenstoffemissionsdaten für wichtige Länder und Sektoren enthält. Dieser Datensatz zeigt saisonale Trends und die Auswirkungen von Ereignissen wie der COVID-19-Pandemie auf die Kohlenstoffemissionen. Die zweite Fallstudie nutzt das IBIS-Modell, um die Quellen und Senken dreier wichtiger Treibhausgase (CO2, CH4 und N2O) in globalen terrestrischen Ökosystemen zu simulieren und bietet Einblicke in ihre langfristigen Trends und Chinas Beiträge. Das Kapitel schließt mit der Betonung der Bedeutung der Verbesserung der Datenüberwachungskapazitäten und der Umsetzung strenger Strategien zur Beschleunigung der Emissionsreduzierung und Verbesserung der Kohlenstoffsenken des Ökosystems. Die Leser erhalten wertvolle Einblicke in die neuesten Methoden zur Überwachung von Klimaindikatoren und die entscheidende Rolle von Daten bei der Gestaltung wirksamer Strategien zur Eindämmung des Klimawandels.
KI-Generiert
Diese Zusammenfassung des Fachinhalts wurde mit Hilfe von KI generiert.
Abstract
Global greenhouse gas concentrations continue to rise, leading to record-breaking global average temperatures. This trend results in an increase in extreme weather events and an acceleration of glacier and ice cap melting, causing sea level rise and posing a threat to biodiversity.
6.1 Background
Global greenhouse gas concentrations continue to rise, leading to record-breaking global average temperatures. This trend results in an increase in extreme weather events and an acceleration of glacier and ice cap melting, causing sea level rise and posing a threat to biodiversity (WMO 2023). The Global Risks Report 2023 indicates that the three greatest risks facing humanity in the next decade are the inability to mitigate climate change, the inability to adapt to climate change, and extreme natural disasters, all of which are closely related to climate change. Climate change is already seriously threatening human survival and development (WEF 2023). The UN Sustainable Development Goals Report 2022 points out that the world is on the edge of climate catastrophes, and the window for avoiding it is closing. Global greenhouse gas emissions are still increasing, and all countries need to take immediate action to shift from the tipping point of climate disaster to a turning point towards sustainable future (United Nations 2022). After announcing carbon peaking and carbon neutrality goals in 2020, China released the National Climate Change Adaptation Strategy 2035 in 2022, promoting enhanced adaptive capacity and proactive responses to climate change.
SDG 13 (Climate Action) aims to take urgent action to combat climate change and its impacts. However, it is currently the most data-deficient among all 17 SDGs, with only about 20% of countries having relevant data (United Nations 2022), and data with spatial and temporal information is even scarcer. Therefore, there is an urgent global need for data that can reflect overall progress towards SDG indicators while providing spatial details and temporal trends to inform decisions on disaster response and climate change mitigation.
Anzeige
The reports from 2019 to 2022 focused on five indicators: disaster losses (SDG 13.1.1), national disaster risk reduction strategies (SDG 13.1.2), proportion of local governments with disaster risk reduction strategies (SDG 13.1.3), greenhouse gas emissions (SDG 13.2.2), and climate change education (SDG 13.3.1). These reports provided indicator calculation methods, spatiotemporal data products, and scientific decision support at both the Chinese and global scales.
This year, we have reviewed the new progress in global greenhouse gas balance in response to SDG 13.2.2, providing scientific data support for formulating greenhouse gas emission reduction strategies.
6.2 Main Contributions
This chapter focuses on the specific goals of SDG 13.2, based on multi-source data such as satellite remote sensing, surveys, and statistics. Through two cases, it provides a model method for monitoring and analyzing indicators for SDG 13, as well as data products with rich spatiotemporal information. Based on this, decision recommendations are proposed (Table 6.1).
Table 6.1
Cases and their main contributions
Target
Tiers
Cases
Contributions
SDG 13.2 Integrate climate change measures into national policies, strategies and planning
Tier I
Global Near-Real-Time Carbon Data
Data product: Daily carbon emission dataset of major countries and sectors worldwide from 2019 to 2022
Tier I
Global fluxes of three greenhouse gases (CO2, CH4, and N2O) from sources and sinks in terrestrial ecosystems
Decision support: Presenting a panoramic map of public awareness of climate change issues in seven major geographical regions of China
6.3 Case Study
6.3.1 Global Near-Real-Time Carbon Data
Target: SDG 13.2 Integrate climate change measures into national policies, strategies and planning.
Background
Anzeige
The main objective of SDG 13 (Climate Action) is to take urgent action to combat climate change and its impacts. It is one of the key goals of the SDGs (United Nations 2015). Phenomena such as global warming pose significant threats to human well-being and the health of the planet’s ecosystems. Therefore, addressing extreme climate events, ecosystem impacts, and climate-related risks is crucial for mitigating economic, social, and environmental losses, and is vital for achieving sustainable development in countries around the world.
In response to the urgent need to combat climate change, the 21st session of the Conference of the Parties (COP 21) to the United Nations Framework Convention on Climate Change (UNFCCC) adopted the Paris Agreement, with the goal of limiting global temperature rise to well below 2 °C above pre-industrial levels, aiming for 1.5 °C. To achieve this, countries demonstrated strong ambitions and determination in climate governance by presenting their national climate change mitigation plans and carbon neutrality timelines. However, assessing the formulation and implementation of these mitigation measures requires more scientific, timely, and accurate high-resolution data.
Currently, the UN Inter-Agency and Expert Group on SDG Indicators (IAEG-SDGs) has established a monitoring indicator system (framework) for SDG 13.2, which includes annual greenhouse gas emissions as a key climate indicator. However, this indicator is insufficient to support the global carbon reduction and carbon neutrality goals. There remains a significant data gap in terms of geographic coverage and timeliness. National carbon emission data is currently based on annual national statistics, which suffer from a time lag of over one year. Moreover, global carbon emissions may have undergone dramatic changes due to unforeseen events such as the COVID-19 pandemic and other global crises. Therefore, establishing near-real-time global carbon emission indicators is crucial for timely, accurate, and reliable carbon emission monitoring and for evaluating SDG 13.
Since 2020, the Carbon Monitor team, led by Tsinghua University, has developed a near-real-time global carbon emission database, which presents the carbon emission dynamics of various countries in a scientific and transparent manner. In the context of the COVID-19 pandemic, the Carbon Monitor team continuously tracked changes in carbon emissions across countries, considering multiple policy objectives such as pandemic control, economic recovery, and carbon emission reduction. This achievement provides real-time, accurate carbon emission information to governments, research institutions, and businesses, facilitating more effective climate action planning and contributing positively to achieving SDG 13.
Data
Global atmospheric reanalysis dataset from 2019 to 2022, provided by the Copernicus Climate Change Service (C3S), with a spatial resolution of 0.25°.
2018 global carbon emission data, sourced from the Emissions Database for Global Atmospheric Research (EDGAR).
2019-2022 global national sectoral (electricity, industry, aviation, ground transportation, residential consumption, shipping) activity data, including energy consumption, industrial activity, global positioning system (GPS) data, and radar data, from national statistical agencies, open-source websites, and operating companies.
2019-2022 TROPOspheric Monitoring Instrument (TROPOMI) NO2 column concentration data from ESA.
Method
Countries are categorized by their carbon emission sources into six major sectors: electricity, industry, residential consumption, ground transportation, aviation, and shipping. Based on multi-source data such as statistical data, satellite remote sensing, and observational data, a near-real-time quantification method for carbon emissions was constructed, assessing the spatiotemporal dynamics of global carbon emissions from 2019 to 2022. This method utilized the linear relationship between carbon emissions and energy activity intensity (such as the relationship between emissions and fuel consumption, electricity generation, driving distance, etc.) to estimate carbon emissions in near-real-time within an uncertain range. The resulting dataset consisted of daily sector-specific carbon emission data for major countries from 2019 to 2022. By constraining the total daily carbon emissions at the national scale with these datasets and using NO2 satellite observation concentration values for spatial distribution corrections, near-real-time daily global carbon emission data was produced. These data were then used to assess SDG 13 indicators and the progress of climate change mitigation from 2019 to 2022.
Results and Analysis
The global and major emitting countries’/regions’ daily carbon emissions from 2019 to 2022 are shown in Fig. 6.1. Daily global carbon emission patterns indicate higher emissions in winter and summer, and lower emissions in spring and autumn, forming a trend similar to that of a galloping horse. The highest global carbon emissions occur in winter (December), and the lowest in spring (April), with emissions rising slightly in summer and increasing rapidly in autumn. This pattern is linked to the concentration of populations in the Northern Hemisphere, where heating and cooling demands are higher in winter and summer. While seasonal variations in monthly emissions differ among countries due to geographic location, the daily emission trends in major carbon-emitting countries still follow the “galloping horse” curve. Additionally, global carbon data reflect a significant drop in anthropogenic carbon emissions in 2020 due to the COVID-19 pandemic, with a 5.4% decrease compared to 2019 (1.9 Gt CO2), and a rebound in 2021 to near pre-pandemic levels, with an increase of 6.3% (2.1 Gt CO2).
Fig. 6.1
Daily carbon emission data of global and major emitting countries/regions (2019–2022)
The global near-real-time carbon emission map (Fig. 6.2) covers over 90% of the world’s countries and regions, with a spatial resolution of 0.1° × 0.1°, providing insights into global carbon emission hotspots and revealing emissions patterns at annual, seasonal, monthly, and weekly scales. Major carbon emission hotspots are concentrated in Eastern North America, Western Europe, South Asia, and Eastern China. High-resolution carbon emission maps enable analysis at finer spatial scales, allowing the study of carbon emission trends at provincial and city levels.
Fig. 6.2
Global near-real-time carbon emission map at the 0.1° × 0.1° daily scale and emission trends for major cities
Based on the UNFCCC and multi-source near-real-time big data, the global national-scale near-real-time carbon emission monitoring and evaluation system was developed, providing timely and accurate reflections of carbon emission responses to emergencies and the latest progress in climate change mitigation actions.
Global CO2 emissions in 2022 increased by 1.5% compared to 2021 (7.9% compared to 2020 and 2.0% compared to 2019), reaching 36.1 Gt CO2. If global emissions remain at the 2022 level, the remaining carbon budget under the 1.5 °C target will be exhausted within the next 2-7 years (with a 67% probability).
The daily carbon emission dynamics of the world and major emitting countries/regions show higher emissions in winter and summer and lower emissions in spring and autumn. This pattern is associated with the high population density in the Northern Hemisphere and the heating and cooling demands in winter and summer.
Discussion and Outlook
Under the SDG 13 monitoring framework, this case study proposes a carbon emission quantification approach based on multi-source activity data, addressing issues such as the time lag, uncertainty, and data accuracy in the current greenhouse gas emission accounting system. This innovative approach enables near-real-time dynamic monitoring of carbon emissions and the construction of a consistent, comparable global dataset of daily sector-specific carbon emissions for major countries, which helps evaluate global carbon reduction and carbon neutrality progress. This approach aids in taking more effective low-carbon actions, providing an important data foundation for optimizing carbon reduction policies and responding to emergencies such as extreme weather, natural disasters, and public crises. It also offers vital data support for evaluating SDG 13 indicators.
Global near-real-time carbon data shows that, although the COVID-19 pandemic led to a record decline in global anthropogenic carbon emissions in 2020, global carbon emissions have since returned to pre-pandemic levels. Despite efforts to reduce fossil fuel use, global carbon emissions continue to rise. If emissions remain at the 2022 level, the remaining carbon budget will further shrink. Therefore, countries must act immediately to fulfill net-zero emission commitments and enhance near-real-time greenhouse gas monitoring capabilities to assess the effectiveness of mitigation measures and climate change mitigation progress.
6.3.2 Global Fluxes of Three Greenhouse Gases (CO2, CH4, and N2O) from Sources and Sinks in Terrestrial Ecosystems
Target: SDG 13.2 Integrate climate change measures into national policies, strategies and planning.
Background
SDG 13.2, “Integrate climate change measures into national policies, strategies and planning”, is one of the important objectives of the SDGs (United Nations 2015), which is directly related to whether human society can take timely and effective measures to curb the continuous increase of greenhouse gas concentrations in the atmosphere, effectively respond to climate change, and maintain the sustainable development of human economic and social systems, industrial and agricultural production, and natural ecosystems.
One of the most important foundations for incorporating initiatives to address climate change into national policies and planning is the accurate quantification of greenhouse gas fluxes from sources and sinks, which is the basis for the formulation of national greenhouse gas emission reduction policies. In this regard, as early as 1996, the IPCC formulated the Guidelines for National Greenhouse Gas Inventories. The guidelines provide different tiers of accounting for greenhouse gas fluxes by sources and sinks in a variety of sectors, including energy, industry, forestry, agriculture and land use change, and waste. Among these, the quantification of greenhouse gas fluxes in terrestrial ecosystems presents the greatest uncertainty. The highest-tier method recommended in the guidelines involves using terrestrial ecosystem models for national-scale inventories. Particularly on a global scale, conducting long-term assessments of the net fluxes of the three greenhouse gases (CO2, CH4, and N2O) in terrestrial ecosystems is critical for effectively managing terrestrial ecosystems, enhancing their carbon sink function, and scientifically formulating climate change mitigation policies.
This case study utilizes the integrated biosphere simulator (IBIS) model, combining with Big Earth data, to simulate the sources and sinks of the three greenhouse gases (CO2, CH4, and N2O) in global terrestrial ecosystems. By analyzing the spatiotemporal patterns of greenhouse gas fluxes in terrestrial ecosystems, this case study evaluates the sources and sinks in China’s terrestrial ecosystems and their contributions to global fluxes, and quantifies the impacts of climate change and human activities on these fluxes. It also identifies the key factors driving the changes. These research results provide a solid scientific basis for the preparation of national communications and biennial update reports under SDG 13.2.
Data
1980-2022 global atmospheric reanalysis dataset, provided by the ECMWF, with a spatial resolution of 9 km.
1980-2022 global land cover dataset (Land-Use Harmonization, LUH2), provided by the University of Maryland, with a spatial resolution of 25 km.
2000-2018 global wetland distribution dataset (Wetland Area and Dynamics for Methane Modeling, WAD2M), provided by the University of Maryland, with a spatial resolution of 25 km.
1980-2015 China Forest Cover Dynamics (CFCD) dataset, provided by Sun Yat-sen University, with a spatial resolution of 1 km.
2000-2020 Asian monsoon region rice distribution dataset, provided by Beijing Normal University, with a spatial resolution of 1 km.
Method
This case study employs the IBIS model (Yuan et al. 2014) to calculate the sources and sinks of the three greenhouse gases (CO2, CH4, and N2O) in global terrestrial ecosystems. The model fully integrates biological, chemical, and physical processes in terrestrial ecosystems, making it one of the few models worldwide capable of simultaneously simulating the sources and sinks of all three greenhouse gases. Since 2019, the IBIS model has been part of the Global Carbon Project, providing annual global terrestrial ecosystem CO2 flux data directly used in IPCC assessment reports. Building on the IBIS model, this case study incorporates the latest Big Earth Data products, including land-use change and vegetation type distribution, to improve the accuracy of global terrestrial ecosystem greenhouse gas flux simulations. Notably, this case study utilizes datasets specifically designed for China to accurately capture the spatiotemporal variations in greenhouse gas fluxes from sources and sinks and analyze China’s contributions to global terrestrial ecosystem fluxes of the three greenhouse gases.
Results and Analysis
This case study first presents the sources and sinks of the three greenhouse gases in global terrestrial ecosystems. For CO2, the global terrestrial ecosystem showed a significant carbon sink with an average emission intensity of 2.36 Pg C/a (Fig. 6.3). However, for CH4 and N2O, the global terrestrial ecosystems were emission sources, with average emission intensities of 173 Tg C/a and 8.9 Tg N/a from 1980 to 2022, respectively. The long-term trends of these three greenhouse gases also showed notable differences. The carbon sink increased significantly from 1980 to 2000, with an annual growth rate of 0.17–0.19 Pg C/a (Fig. 6.4a). However, since 2000, the growth of the global terrestrial ecosystem carbon sink has stagnated and even shown a slight declining trend. CH4 emissions exhibited a pattern of initial increase followed by a decrease, with the turning point occurring around 2012 (Fig. 6.4b ). N2O emissions increased significantly from 1980 to 2016 but began to decline after 2016 (Fig. 6.4c ).
Fig. 6.3
Global average of three greenhouse gas fluxes from sinks in terrestrial ecosystems from 2010 to 2022
Long-term trends in the fluxes of three greenhouse gas sources and sinks in global terrestrial ecosystems. Note The numbers in the figure indicate the trend over the time period, where * indicates that the trend is statistically significant (p < 0.05)
China’s terrestrial ecosystem greenhouse gas emissions also showed significant long-term trends. Between 1980 and 2022, the average carbon sink strength of China’s terrestrial ecosystems was 0.27 Pg C/a, accounting for 11% of the global carbon sink. Notably, thanks to the large-scale ecological projects implemented by China, its terrestrial carbon sink has increased more than the global carbon sink, and thus its contribution to the global carbon sink has also shown a significant increase. The average emission intensity of China’s terrestrial CH4 was 11.40 Tg C/a, and the emission has shown a decreasing and then increasing trend since 1980, which is consistent with the change in China’s rice cultivation area. China’s terrestrial ecosystems contributed 6.5% of global terrestrial CH4 emissions, with this contribution also following a pattern of decline and subsequent increase. The average intensity of terrestrial N2O emissions in China was 1.25 Tg N/a. Since 2016, environmental policies implemented by the Chinese government to reduce fertilizer use in agriculture have led to a significant decline in N2O emissions. Between 2010 and 2022, China’s terrestrial ecosystems contributed 14.66% of global N2O emissions (Fig. 6.5b).
Fig. 6.5
a Sources and sinks of the three greenhouse gases in China’s terrestrial ecosystems and b their proportional contribution to the global total
Based on the IBIS model and Big Earth Data, this case study comprehensively assessed the intensity and long-term trends of the sources and sinks of the three greenhouse gases (CO2, CH4, and N2O) in global terrestrial ecosystems, with a focus on quantifying China’s contributions to global fluxes.
Compared to the global average, the carbon sink in China’s terrestrial ecosystems exhibited a more significant increasing trend, leading to a notable rise in its contribution to the global terrestrial carbon sink since 1980.
The CH4 emissions from global terrestrial ecosystems showed a trend of initial decline followed by an increase since 1980, and China’s terrestrial CH4 fluxes exhibited a similar pattern.
The N2O emissions from China’s terrestrial ecosystems began to show a significant decline starting in 2016, which also contributed to a gradual reduction in global N2O fluxes.
Discussion and Outlook
This case study, based on the IBIS model and the latest Big Earth Data, simulated the sources and sinks of the three greenhouse gases (CO2, CH4, and N2O) in global terrestrial ecosystems, with a particular focus on China’s contributions to global fluxes and their changing trends. It lays a methodological foundation for comprehensive assessments of terrestrial ecosystem greenhouse gas fluxes at national and global scales. More importantly, according to the Paris Agreement, starting in 2024, all parties are required to submit national greenhouse gas inventories every two years. This means that developing countries, including China, will need to report their greenhouse gas emissions regularly, just as developed countries do. The accounting of global terrestrial ecosystem greenhouse gas fluxes of the three greenhouse gases in this case study provides an independent reference for other countries’ emission inventories. Furthermore, the methodology used in this case study can directly serve as a foundational approach for developing countries to compile national greenhouse gas inventories, which is highly significant for achieving SDG 13.2.
At the same time, the changes in China’s contributions to global greenhouse gas fluxes demonstrated in this case study highlight China’s outstanding efforts in addressing climate change. On the one hand, large-scale ecological projects implemented by the Chinese government since the early 1980s have significantly increased China’s terrestrial carbon sinks. This increase has exceeded the global average, thereby significantly enhancing China’s contribution to global carbon sinks. On the other hand, since 2016, the Chinese government has significantly reduced the use of chemical fertilizers in agriculture, resulting in a marked decrease in terrestrial N2O emissions, which has played a critical role in reducing global emissions. However, it should be noted that the recent expansion of rice cultivation areas has led to a significant increase in CH4 emissions from China’s terrestrial ecosystems. Therefore, strengthening rice management to mitigate CH4 emissions is crucial for future efforts to address climate change.
6.4 Summary
This chapter focuses on addressing climate change proactively by employing Big Earth Data methodologies to calculate the greenhouse gas emissions indicator (SDG 13.2.2) and provide corresponding spatiotemporal data products. Based on the data and findings presented, we find that global greenhouse gas emissions are still under great pressure. In contrast, China’s terrestrial ecosystem carbon sinks exhibit a more significant increasing trend compared to the global average, resulting in a notable rise in its contribution to global carbon sinks since 1980. Regarding indicator evaluation, we suggest modifying the SDG 13.2.2 indicator to better assess countries of varying scales by adopting metrics such as per capita greenhouse gas emissions or emissions per unit of GDP.
While both China and the global community have made progress in enhancing ecosystem carbon sinks under SDG 13, future efforts must prioritize monitoring greenhouse gas reduction implementations. On the one hand, it is essential to improve data monitoring capabilities for greenhouse gas emissions. On the other hand, strong policies must be formulated to accelerate emission reductions while simultaneously enhancing forest conservation and farmland management to increase ecosystems’ capacity to sequester greenhouse gases.
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits any noncommercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if you modified the licensed material. You do not have permission under this license to share adapted material derived from this chapter or parts of it.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Yuan WP, Liu D, Dong WJ et al (2014) Multiyear precipitation reduction strongly decreases carbon uptake over Northern China [J]. J Geophys Res Biogeosci 119(5):881–896CrossRef
