# Greenhouse Gas Budgets of Central and West Asia (2000–2020): A Significant Net Source to the Atmosphere

Xiaoyu Qin, Hanqin Tian, Josep G Canadell, Yu Shi, Shufen Pan, Ana Bastos, Philippe Ciais, Monica Crippa, Naiqing Pan, Prabir K Patra, et al.

► To cite this version:

Xiaoyu Qin, Hanqin Tian, Josep G Canadell, Yu Shi, Shufen Pan, et al. Greenhouse Gas Budgets of Central and West Asia (2000–2020): A Significant Net Source to the Atmosphere. Global Biogeochemical Cycles, 2025, 39 (3), 10.1029/2024gb008370. hal-04975585

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# Global Biogeochemical Cycles®

## RESEARCH ARTICLE

10.1029/2024GB008370

**Key Points:**

* Central and West Asia is a major greenhouse gas (GHG) source, emitting ~4,234 Mt CO₂eq yr⁻¹ in the 2010s based on Global Warming Potential

* CO₂ from fossil fuel combustion contributed 59% of the total; CH₄ contributed 32%, with a much higher fraction for shorter timeframes

* Overall, Central and West Asia contributed ~8% of the global GHG budget in the 2010s, with ~10% of global CO₂, ~7% of CH₄, and ~3% of N₂O

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

H. Tian,
hanqin.tian@bc.edu

**Citation:**

Qin, X., Tian, H., Canadell, J. G., Shi, Y., Pan, S., Bastos, A., et al. (2025). Greenhouse gas budgets of Central and West Asia (2000–2020): A significant net source to the atmosphere. *Global Biogeochemical Cycles*, *39*, e2024GB008370. https://doi.org/10.1029/2024GB008370

Received 26 SEP 2024
Accepted 7 FEB 2025

**Author Contributions:**

**Conceptualization:** Xiaoyu Qin,
Hanqin Tian, Josep G. Canadell,
Philippe Ciais

**Data curation:** Xiaoyu Qin

**Formal analysis:** Xiaoyu Qin

**Funding acquisition:** Hanqin Tian

**Investigation:** Xiaoyu Qin, Hanqin Tian

**Methodology:** Xiaoyu Qin, Hanqin Tian,
Josep G. Canadell, Yu Shi, Ana Bastos,
Philippe Ciais, Monica Crippa,
Naiqing Pan

**Project administration:** Hanqin Tian

**Resources:** Hanqin Tian, Josep
G. Canadell, Shufen Pan, Ana Bastos,

© 2025. The Author(s).
This is an open access article under the
terms of the Creative Commons
Attribution License, which permits use,
distribution and reproduction in any
medium, provided the original work is
properly cited.

<figure><img src="image_2.png" /></figure>

## Greenhouse Gas Budgets of Central and West Asia (2000–2020): A Significant Net Source to the Atmosphere

Xiaoyu Qin¹,², Hanqin Tian³,⁴ Ⓓ, Josep G. Canadell⁵ Ⓓ, Yu Shi³,⁶, Shufen Pan³,⁷, Ana Bastos⁸ Ⓓ, Philippe Ciais⁹ Ⓓ, Monica Crippa¹⁰, Naiqing Pan³, Prabir K. Patra¹¹,¹² Ⓓ, Benjamin Poulter¹³ Ⓓ, Marielle Saunois⁹ Ⓓ, and Stephen Sitch¹⁴ Ⓓ

¹State Key Laboratory for Ecological Security of Regions and Cities, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China, ²University of Chinese Academy of Sciences, Beijing, China, ³Center for Earth System Science and Global Sustainability (CES3), Schiller Institute for Integrated Science and Society, Boston College, Chestnut Hill, MA, USA, ⁴Department of Earth and Environmental Sciences, Boston College, Chestnut Hill, MA, USA, ⁵Global Carbon Project, CSIRO Environment, Canberra, ACT, Australia, ⁶College of Urban and Environmental Sciences, Institute of Carbon Neutrality, Sino-French Institute of Earth System Sciences, Peking University, Beijing, China, ⁷Department of Engineering, Boston College, Chestnut Hill, MA, USA, ⁸Department Biogeochemical Integration, Max-Planck-Institut für Biogeochemie, Jena, Germany, ⁹Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay, Gif-sur-Yvette, France, ¹⁰European Commission, Joint Research Centre (JRC), Ispra, Italy, ¹¹Research Institute for Global Change, JAMSTEC, Yokohama, Japan, ¹²Research Institute for Humanity and Nature, Kyoto, Japan, ¹³Biospheric Sciences Lab, NASA Goddard Space Flight Center, Greenbelt, MD, USA, ¹⁴College of Life and Environmental Sciences, University of Exeter, Exeter, UK

**Abstract** This study provides the first comprehensive quantification of three major greenhouse gases (GHGs, including CO₂, CH₄, and N₂O) budgets for Central and West Asia (CWA) from 2000 to 2020, including contributions from fossil fuels, industry, and managed and unmanaged terrestrial ecosystems. We use bottom-up (BU: inventories and process-based models) and top-down approaches (TD: atmospheric inversions) to elucidate CWA's GHG budget and its changes. BU and TD budgets consistently show that CWA was a significant and growing GHG source during the 2010s: average net emissions were 4,175 (range: 4,055–4,301) Tg CO₂eq yr⁻¹ based on BU and using global warming potentials over a 100-year period (GWP100), and slightly higher net emissions of 4,293 (3,760–4,826) Tg CO₂eq yr⁻¹ based on TD. BU estimates show that CO₂ emissions from fossil fuel combustion and fugitive releases were the dominant source, accounting for 61% of the total budget in the 2010s, with 2,554 (2,526–2,582) Tg CO₂eq yr⁻¹. Terrestrial natural ecosystems were a weak CO₂ sink and sources of CH₄ and N₂O, which together resulted in a decadal mean net GHG emission of 220.5 (114.5–332.8) Tg CO₂eq yr⁻¹. Non-CO₂ gases, primarily CH₄, contributed significantly to the region's GHG emissions, accounting for 32% (BU) and 24% (TD) of CWA's total GHG budget under GWP100, and increasing to 57% (BU) and 49% (TD) with GWP20, highlighting CH₄ stronger warming impact over shorter timescales. Overall, CWA contributed about 8% of global net GHG emissions in the 2010s, with about 10% of global CO₂, 7% of CH₄, and 3% of N₂O.

**Plain Language Summary** There is a significant gap in understanding greenhouse gas (GHG) emissions in Central and West Asia (CWA). This study provides the first comprehensive quantification of three major gases—carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)—from 2000 to 2020. Using both bottom-up (inventories and process-based models) and top-down (atmospheric inversions) methods, we found that CWA was a major source of GHGs, with average emissions of about 4,234 million tons of CO₂ equivalent per year in the 2010s. CO₂ emissions from burning fossil fuels was the largest contributor, making up 61% of the total budget. Non-CO₂ gases like CH₄ also played a significant role, accounting for 32% of GHG emissions based on 100-year global warming potential (GWP100), which increased to 57% when considered over 20 years (GWP20). Among the 24 countries in the region, the top 10 contributed 84% of the GHG emissions, mainly from fossil fuel use in electricity, oil and gas extraction, and transportation. Overall, CWA accounted for about 8% of global GHG emissions in the 2010s. Additionally, uncertainties in bottom-up and top-down estimates highlight the need for local efforts in GHG measurement, targeting both human activities and natural ecosystems.
# 1. Introduction

Greenhouse gas (GHG) emissions from human activities have unequivocally led to global warming, with the global average surface temperature for the period 2014–2023 being 1.20 ± 0.12°C higher than the pre-industrial average (1850–1900) (Forster et al., 2023; World Meteorological Organization (WMO), 2024). The Central and West Asia (CWA) region has experienced temperature increases at twice the global average, alongside increasingly scarce and unpredictable rainfall (Intergovernmental Panel on Climate Change (IPCC), 2022; Z. Hu et al., 2014; Zittis et al., 2022). This is leading to critical climate impacts—such as glacier melt in the Pamirs affecting water availability (Barandun et al., 2020), intensified dust storms in Iran and the Persian Gulf due to severe droughts (Dastorani & Jafari, 2019), and escalating water scarcity threatening agriculture and food security in Saudi Arabia and Yemen (Al-Zahrani et al., 2019; Baig et al., 2019). These impacts affect human activities and natural ecosystems, and thereby the GHG balance of the region. Yet, there remains a significant gap in quantifying the region's GHG budget.

Only a few countries in the CWA region, such as Cyprus, Turkey, and Kazakhstan, are Annex I countries, and have consistently submitted annual national GHG inventory reports to the United Nations Framework Convention on Climate Change (UNFCCC), primarily covering emissions from the energy sector and agriculture, and land use changes. However, detailed estimates from industrial, agricultural, and waste sectors are often omitted (UNFCCC, 2022). In contrast, most countries in the CWA region are non-Annex I countries, which constitute 73.1% of the region's Gross Domestic Product (GDP) and 77.8% of its population in the decade 2010–2019 (United Nations, 2024; World Bank, 2024). These countries have submitted GHG reports sporadically, with most ceasing updates in 2015, except for Israel, Georgia, Jordan, and Kuwait (UNFCCC, 2022). This lack of comprehensive GHG reporting in the CWA region hinders effective climate action and adaptation strategies.

In addition to the significant gap in GHG accounting in academic studies and national reports, recent research highlights troubling trends in methane ($CH_4$) emissions from CWA countries. An increasing number of ultra-emitters of $CH_4$ from oil and gas production and transmission have been detected across Turkmenistan, Iraq, Kuwait, Saudi Arabia, and Oman through satellite measurements with enhanced spatial and magnitude resolution (Lauvaux et al., 2022; Thorpe et al., 2023), highlighting the region's high contribution to global $CH_4$ emissions. Additionally, a recent global assessment of $CH_4$ emissions from coal, oil, and gas exploitation, compiling data from various inventories and global atmospheric inversions, found larger discrepancies in emissions estimates for the oil/gas sector across inventories (Tibrewal et al., 2024). Moreover, emissions reported to the UNFCCC were lower than those estimated by other inventories and inversion methods, further highlighting the critical gap in the current understanding of the total GHG impact from the CWA region. Given the region's heavy reliance on hydrocarbon extraction and exports, which form a significant part of its GDP (IMF, 2024), and its considerable role in the global energy market—producing 35.0% of the world's oil, 19.4% of its natural gas, and 2.3% of its coal over the past two decades (EIA, 2024)—there is an urgent need for up-to-date and comprehensive assessments of its GHG budgets.

The REgional Carbon Cycle Assessment and Processes (RECCAP) project was initiated to provide comprehensive regional assessments of carbon (C) budgets worldwide, covering 10 terrestrial and 4 ocean regions for the period 1990–2009 (Canadell et al., 2011). However, in the first phase of RECCAP, the CWA region was not covered. To address this gap, the second phase of RECCAP project (RECCAP2), launched in 2017 under the Global Carbon Project (GCP), aims to develop detailed multi-GHG budgets for the period 2010–2019, including the previously overlooked CWA region (Ciais et al., 2022). RECCAP2 provides a first time opportunity to quantify anthropogenic emissions and natural sinks and sources of multiple GHGs for the CWA region.

Our study focuses on the CWA region as defined by RECCAP2, following its guidelines (Ciais et al., 2022) to fill the longstanding knowledge gap in this region. We have incorporated observation-based assessments from atmospheric inversions (referred to as the TD approach) and estimates from multi-source land models (referred to as the BU approach), in conjunction with national emission inventories, to quantify the magnitudes and trends of the three most important GHGs—carbon dioxide ($CO_2$), $CH_4$, and nitrous oxide ($N_2O$)—and their overall warming effects in the CWA region from 2000 to 2020. This is the first comprehensive GHG budget for the region, which clarifies regional differences in global estimates and contribute to addressing the longstanding gap in emission inventories for developing countries, thereby supporting the Enhanced Transparency Framework and reporting of the Paris Agreement.

Naiqing Pan, Benjamin Poulter, Marielle Saunois, Stephen Sitch

**Software:** Xiaoyu Qin, Naiqing Pan
**Supervision:** Hanqin Tian
**Validation:** Xiaoyu Qin
**Visualization:** Xiaoyu Qin, Hanqin Tian
**Writing – original draft:** Xiaoyu Qin, Hanqin Tian, Josep G. Canadell, Yu Shi
**Writing – review & editing:** Xiaoyu Qin, Hanqin Tian, Josep G. Canadell, Yu Shi, Shufen Pan, Ana Bastos, Philippe Ciais, Monica Crippa, Prabir K. Patra, Benjamin Poulter, Marielle Saunois, Stephen Sitch
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Land cover from Friedl and Sulla-Menashe (2022) and political boundary of countries in Central and West Asia (South, 2011).</figcaption></figure>

## 2. Methods

### 2.1. Study Area

Our study area focuses on the CWA region, as delineated in the RECCAP2 regional classification by Ciais et al. (2022). This vast and diverse region spans 24 countries in Asia, including Afghanistan (AFG), Armenia (ARM), Azerbaijan (AZE), Bahrain (BHR), Cyprus (CYP), Georgia (GEO), Iran (IRN), Iraq (IRQ), Israel (ISR), Jordan (JOR), Kazakhstan (KAZ), Kuwait (KWT), Kyrgyzstan (KGZ), Lebanon (LBN), Oman (OMN), Qatar (QAT), Saudi Arabia (SAU), Syria (SYR), Tajikistan (TJK), Turkey (TUR), Turkmenistan (TKM), United Arab Emirates (ARE), Uzbekistan (UZB), and Yemen (YEM) (Figure 1). The total land area of CWA is approximately 1.1 × 10⁷ km² (Food and Agriculture Organization of the United Nations (FAO), 2023), which encompasses various land cover and land use types, such as forests, shrublands, grasslands, wetlands, croplands, deserts, and urban settlements (Friedl & Sulla-Menashe, 2022; Figure 1).

### 2.2. Overview of the Framework and Methods Used for the GHG Budget

The accounting framework for the GHG budgets was constructed based on the analytical framework proposed by Ciais et al. (2022) and then updated to account for the characteristics of the CWA region and expanded to CH₄ and N₂O (Figure 2). The central idea is to provide a full assessment of the annual budgets for the three main GHGs—CO₂, N₂O, and CH₄—and their combined climate impact using the global warming potentials of the three gases into the unit of CO₂ equivalent. We consider both anthropogenic and natural sources and sinks from 2000 to 2020. Various methods were employed to estimate the GHG budgets and their component fluxes, which can be categorized into two main categories: BU and TD approaches. The BU and TD approached budgets are independent and complementary to each other (Figure 2). The BU methods provide detailed estimates for emissions or removals for each gas in each sector at both national and regional levels. The sum of all sectors' BU estimates gives us the BU budgets. The TD methods estimate the net balance of total budgets; however, because they do not distinguish well between different emission sources and have coarser resolution (the grid area may be larger than
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Accounting framework of the greenhouse-gas budgets. The sector codes for anthropogenic sources (Energy, Industry, Agriculture, and Waste) use the Intergovernmental Panel on Climate Change codes designated for each sector. For natural sources and sinks (Terrestrial Natural Ecosystems, Inland Waters, and Geological Sources), a self-defined sector code system has been developed, following the style and sequential structure of the anthropogenic codes. This approach was adopted due to the lack of a standardized system for categorizing natural sources and sinks. Black braces indicate the fluxes included in the regional and national bottom-up (BU) budgets. The BU estimates of 3 GHG fluxes are categorized into 7 sectors: energy (yellow), industry (blue), agriculture (dark green), waste (brown), terrestrial natural ecosystems (light green), inland waters (light blue), and geological seepage (light gray). Fluxes of each gas are shown with colored arrows: CO₂ (sky blue), CH₄ (olive green), and N₂O (purple). The dotted line with double-headed arrows represents the use of atmospheric inversion (top-down) budgets to match the BU budgets at the regional level.</figcaption></figure>

some countries in the CWA region, such as Qatar. Table 1), we only included TD approached budgets at the regional level to compare with the BU approached budgets.

We considered four types of BU methods to quantify the national and regional CO₂, CH₄ and N₂O budgets and their component fluxes (Table 1) since there is no single observation-based BU method that comprehensively covers all GHG fluxes (Ciais et al., 2022). Anthropogenic emissions were derived from inventories using activity and emission-factor (EF) input data to calculate GHG emissions induced by human activities across different sectors. These inventory databases have been widely used in regional and national studies (Mostefaoui et al., 2024; Murray-Tortarolo et al., 2024; Petrescu et al., 2023; X. Wang et al., 2024). GHG emissions and removals by terrestrial natural ecosystems were derived from the results of process-based terrestrial biosphere models (TBMs). These models are advantageous for their ability to simulate key processes that influence GHG production and emission, and for integrating various driving factors such as climate change, land use and cover change, and atmospheric CO₂ concentration (Ciais et al., 2022). However, they contain many parameters, and their global total budgets may not be consistent with the global growth rates of the GHGs considered, unlike inversions which fit the atmospheric data by construction. Additionally, for GHG emissions from inland waters, we utilized estimates from the RECCAP2 Inland Water assessment (Lauerwald et al., 2023), which synthesized all available literature estimates and homogenized them with regard to water surface area and the effects of seasonal ice cover. For geological CH₄ emissions, we derived the data from Etiope et al. (2019), who developed
<table><caption>**Table 1**
List of Bottom-Up and Top-Down Methods Used</caption><thead><tr><th>Data set name</th><th>Method</th><th>CO<sub>2</sub></th><th>CH<sub>4</sub></th><th>N<sub>2</sub>O</th><th>Spatial resolution</th><th>Time period</th><th>Reference</th></tr></thead><tbody><tr><td colspan="8"><strong>Inventory databases</strong></td></tr><tr><td>EDGAR v8</td><td>BU</td><td>×</td><td>×</td><td>×</td><td>Country level</td><td>2000–2020</td><td>Crippa et al. (2023)</td></tr><tr><td>CEDS v2024_04_01</td><td>BU</td><td>×</td><td>×</td><td>×</td><td>Country level</td><td>2000–2020</td><td>Hoesly and Smith (2024)</td></tr><tr><td>FAOSTAT 2023</td><td>BU</td><td></td><td>×</td><td>×</td><td>Country level</td><td>2000–2020</td><td>Tubiello et al. (2021, 2022)</td></tr><tr><td>GFED v4.1s</td><td>BU</td><td></td><td>×</td><td>×</td><td>0.25° × 0.25°</td><td>2000–2020</td><td>Van Der Werf et al. (2017)</td></tr><tr><td colspan="8"><strong>Process-based terrestrial biosphere models</strong></td></tr><tr><td>TRENDY v11 ensemble</td><td>BU</td><td>×</td><td></td><td></td><td>0.5° × 0.5°</td><td>2000–2020</td><td>Friedlingstein et al. (2022)</td></tr><tr><td>Global Methane Budget terrestrial biosphere models' ensemble</td><td>BU</td><td></td><td>×</td><td></td><td>0.5° × 0.5°</td><td>2000–2020</td><td>Saunois et al. (2024) and Martinez et al. (2024)</td></tr><tr><td>NMIP2 ensemble</td><td>BU</td><td></td><td></td><td>×</td><td>0.5° × 0.5°</td><td>2000–2020</td><td>Tian et al. (2018, 2024)</td></tr><tr><td colspan="8"><strong>Inland waters</strong></td></tr><tr><td>Global inland water GHG emissions</td><td>BU</td><td>×</td><td>×</td><td>×</td><td>Regional</td><td>-</td><td>Lauerwald et al. (2023)</td></tr><tr><td colspan="8"><strong>Geological seepage</strong></td></tr><tr><td>Gridded maps of global geological CH<sub>4</sub> emissions</td><td>BU</td><td></td><td>×</td><td></td><td>1.0° × 1.0°</td><td>-</td><td>Etiope et al. (2019)</td></tr><tr><td colspan="8"><strong>Atmospheric inversions models</strong></td></tr><tr><td>Global Carbon Budget inversions' ensemble</td><td>TD</td><td>×</td><td></td><td></td><td>1.0° × 1.0°–6.0° × 4.0°</td><td>2000–2020</td><td>Friedlingstein et al. (2022) and Luijkx et al. (2023)</td></tr><tr><td>Global Methane Budget inversions' ensemble</td><td>TD</td><td></td><td>×</td><td></td><td>1.0° × 1.0°–6.0° × 4.0°</td><td>2000–2020</td><td>Saunois et al. (2024) and Martinez et al. (2024)</td></tr><tr><td>Global Nitrous oxide Budget inversions' ensemble</td><td>TD</td><td></td><td></td><td>×</td><td>2.8° × 2.8°–5.6° × 5.6°</td><td>2000–2020</td><td>Tian et al. (2024)</td></tr></tbody></table>

gridded CH₄ emissions from four main categories of natural geological sources based on the areal distribution, CH₄ EFs, and its stable C isotope composition from the literature. To include all available estimates of each GHG flux from different sources and to present the budgets simply and clearly, we grouped the BU-estimated fluxes into seven sectors based on their origin: energy, industry, agriculture, waste, terrestrial natural ecosystems, inland waters, and geological seepage (Figure 2). Fluxes were different among the three GHGs.

Atmospheric inversion models, the TD approach, utilize measurements of atmospheric CO₂/N₂O/CH₄ mixing ratios with atmospheric transport models, driven by meteorological fields, to estimate the emissions of CO₂/N₂O/CH₄ (Friedlingstein et al., 2022; Saunois et al., 2024; Tian et al., 2024). Here, we considered five atmospheric global inversions for CO₂ budgets, five inversions for CH₄ budgets and four inversions for N₂O fluxes that covered the same period with this study (Table 1). All of these inversions used near-surface observations from ground-based networks. More details about the data and methods we used for each flux are described in the following sections: CO₂ (Section 2.3), CH₄ (Section 2.4), N₂O (Section 2.5), calculation of synthetic GHG budgets (Section 2.6), data analysis and uncertainty estimates (Section 2.7).

## 2.3. CO₂ Budget

### 2.3.1. Bottom-Up Approach

The BU approach to the CO₂ budget included anthropogenic CO₂ emissions and natural sources and sinks of CO₂. Anthropogenic CO₂ emissions were categorized into four sectors: energy, industry, agriculture, and waste. The natural CO₂ budget comprised two components: net ecosystem exchange (NEE) and inland water outgassing. The coverage and estimation procedures for each sector are described below, with more information provided in Table S1 in Supporting Information S1.
### 2.3.1.1. Anthropogenic CO₂ Emissions

For the energy sector, CO₂ emissions were primarily from the combustion and fugitive releases of fossil fuels. These were further broken down into: electricity and heat generation, petroleum refining processes, transportation (including road, rail, air, and water), buildings, and fugitive emissions from the exploitation and transportation of coal, oil, and natural gas. The CO₂ emissions from each of these sub-sectors were derived from Emissions Database for Global Atmospheric Research (EDGAR v8, Crippa et al., 2023) and Community Emissions Data System (CEDS v2024_04_01, Hoesly & Smith, 2024), the latest versions of these global inventories. We synthesized the estimates from the two data sets using IPCC category codes reported in their estimates and the mapping guidelines from CEDS (Hoesly et al., 2018). Then, the average of these two estimates was calculated for regional and national emissions during 2000–2020.

The industry sector was analyzed by examining CO₂ emissions from fuel combustion and process emissions from manufacturing activities. Specifically, we considered emissions from the production of cement, lime, glass, and chemicals such as ammonia and urea, which were produced in significant quantities in the CWA countries due to the availability of low-cost natural gas, a key feedstock for nitrogen-based fertilizers (Mathews, 2022; Stastista, 2024a, 2024b). We also included emissions from the production of adipic and nitric acid, iron, steel, and other metals, as well as the industrial use of carbonates. Additionally, emissions from the use of fossil fuels in producing non-energy products, including lubricants, waxes, and solvents, were accounted for. CO₂ emissions from glass production and carbonates application were only available from EDGAR v8 (Crippa et al., 2023), while the estimates of remaining subsectors' emissions were derived from the synthesis of EDGAR v8 (Crippa et al., 2023) and CEDS v2024_04_01 (Hoesly & Smith, 2024).

For the agriculture sector, significant sources of CO₂ emissions include the application of lime to reduce soil acidity and the use of urea during fertilization (West & McBride, 2005; Zamanian et al., 2018, 2021). The estimates of CO₂ emissions from liming and urea application were obtained from EDGAR v8 (Crippa et al., 2023). Additionally, we considered the net C fluxes within croplands, including CO₂ absorption via photosynthesis and its release through respiration and agricultural biomass burning. More details of these processes is provided in Section 2.3.1.2 of our paper.

For the waste sector, CO₂ emissions from incineration and open burning of waste in CWA were estimated using the synthesis of EDGAR v8 (Crippa et al., 2023) and CEDS v2024_04_01 (Hoesly & Smith, 2024). The incineration-induced CO₂ is the emissions arising from the controlled combustion of waste without energy recovery. Open burning refers to the uncontrolled burning of waste materials, resulting in the direct release of emissions into the atmosphere.

### 2.3.1.2. Natural Sinks and Sources of CO₂

In the RECCAP2 protocol, the net exchange of CO₂ between terrestrial ecosystems and the atmosphere over it was defined as NEE, indicating whether the terrestrial natural ecosystems acted as a C sink (absorbing more C than it releases) or a C source (releasing more C than it absorbs) (Ciais et al., 2022). The NEE of the CWA region was estimated based on the TRENDY v11 data set simulation 3 (S3) outputs, where NEE was referred to as the net biome productivity (NBP). NBP describes the net flux of CO₂ between land and atmosphere, considering the total biomass produced through photosynthesis and the C lost through processes like autotrophic respiration, heterotrophic respiration, and emissions from disturbance, for example, wildfires, human-ignited fire, harvest, grazing, land-use and land cover changes and lateral fluxes (Friedlingstein et al., 2022). We note that the grid-cell level NBP data might raise concerns about including CO₂ contributions from cropland. However, because the CO₂ absorbed during photosynthesis offsets the CO₂ released through respiration and biomass burning in cropland, the cropland NBP could be considered C neutral (IPCC, 2022). Emissions from human activities from crop fields, like lime and urea application were included in anthropogenic CO₂ emissions (Section 2.3.1.1), allowing grid-cell level NBP to represent the NEE of terrestrial natural ecosystems. Fifteen TRENDY models were selected based on their coverage of the CWA region and provision annual values for 2000 to 2020 (Table S1 in Supporting Information S1). Each model encompassed distinct C cycle processes and complexities and was forced with the same time-series of climatological data, atmospheric CO₂ concentrations, and land-cover distributions to accurately simulate the present-day global C cycle. The mean NBP was calculated by averaging the computed regional and national values at their original resolution. For map graphing, the nearest interpolation method was used to adjust gridded fluxes from models with resolutions coarser than 0.5° × 0.5°.
The CO₂ emissions from inland waters were calculated based on existing published synthesized global data sets in RECCAP2 (Lauerwald et al., 2023), which homogenized existing estimates based on surface water areas and further corrected for the effects of seasonal ice cover. For CWA, inland water CO₂ emissions were sourced from four types of water bodies: rivers, natural lakes, reservoirs, and lakes regulated by dams. For each water type, 5, 4, 8, and 5 global literature estimates were synthesized, respectively (see Supporting Information S3 in Lauerwald et al., 2023). Because the available estimates are mostly static or with short temporal coverage, we assumed no interannual variations for emissions from inland waters during 2000–2020. The regional emissions for each water type were adopted from Table S1 in Lauerwald et al. (2023). National values for each water type were derived by allocating the regional values to each country based on the surface area of each type of water body in each country (rivers: Allen & Pavelsky, 2018; lakes, reservoirs, and lakes regulated by dams: Messager et al., 2016).

### 2.3.2. Top-Down Approach

Five atmospheric inversion systems were selected from the Global Carbon Budget (GCB, Friedlingstein et al., 2022; Luijkx et al., 2023) project to infer the TD estimates of the land-atmosphere CO₂ flux in the CWA region. The selection criteria were based on whether they provided annual estimates covering the period from 2000 to 2020, or at least 8 years of data within the decade, enabling the inversion results to be compared with the BU budgets (Saunois et al., 2024). The five inversions chosen were: the Copernicus Atmosphere Monitoring Service (CAMS, Chevallier et al., 2005); Jena CarboScope (also referred to as sEXTocNEET, Rödenbeck et al., 2018); Nonhydrostatic Icosahedral Atmospheric Model-based Inverse Simulation for Monitoring CO₂ (NISMON-CO₂, Niwa et al., 2022); UoE in situ (UoE, Feng et al., 2016); and Carbon-Tracker Europe (CTE, Van Der Laan-Luijkx et al., 2017).

Atmospheric CO₂ observations for these inversions were derived from multiple data sets and platforms. Specifically, CAMS, NISMON-CO₂, UoE, and CTE utilized hourly resolution measurements from the CO₂ GLOBALVIEWplus v7.0 ObsPack (Schuldt, Mund, et al., 2021) and the CO₂ NRT v7.2 ObsPack (Schuldt et al., 2022). Additionally, CAMS applied the CO₂ observations archived at the World Data Center for Greenhouse Gases (WDCGG) under the World Meteorological Organization Global Atmospheric Watch (WMO-GAW) program (Japan Meteorological Agency (JMA), 2022), as well as the data from the Integrated Carbon Observation System Atmosphere Thematic Center (ICOS ATC, CAMS, 2022). sEXTocNEET incorporated both flask samples and hourly measurements from multiple institutes, with outliers removed using a ±2 standard deviation criterion (Friedlingstein et al., 2022).

We calculated the ensemble mean of the five inversion estimates to assess the TD CO₂ budget. This ensemble mean was calculated by averaging the inversions' regional values, and each was computed individually at the original resolution. For mapping purposes, the nearest-neighbor interpolation was applied to integrate the fluxes from the original model resolution to a 1° × 1° resolution.

## 2.4. CH₄ Budget

### 2.4.1. Bottom-Up Approach

The BU approach to the CH₄ budget includes anthropogenic CH₄ emissions and natural sources and sinks of CH₄. The framework for categorizing anthropogenic CH₄ emissions was similar to that used for anthropogenic CO₂ emissions, dividing the emissions into four sectors: energy, industry, agriculture, and waste. The natural sources and sinks of CH₄ comprised four components based on their origins: wetlands, wildfires, inland waters, and geological seepages. The coverage and estimation procedures for each sector and component are described below, with more information provided in Table S2 in Supporting Information S1.

#### 2.4.1.1. Anthropogenic CH₄ Emissions

For the energy sector, the sources of CH₄ emissions were identical to those of CO₂ emissions, with estimations derived from the synthesis of EDGAR v8 (Crippa et al., 2023) and CEDS v2024_04_01 (Hoesly & Smith, 2024). The fugitive emissions of CH₄ are particularly important for CWA, as previous studies have highlighted issues related to CH₄ leakage and ultra-emissions from the exploitation and transportation of coal, oil, and natural gas (Lauvaux et al., 2022; Thorpe et al., 2023; Tibrewal et al., 2024).
For the industry sector, CH₄ emissions included those resulting from the combustion of fuels in industrial processes, the production of various inorganic and organic chemicals, and metal production. These estimations were also obtained from the synthesis of EDGAR v8 (Crippa et al., 2023) and CEDS v2024_04_01 (Hoesly & Smith, 2024).

For the agriculture sector, CH₄ estimates specifically included emissions from enteric fermentation, manure management, rice cultivation, and agricultural waste burning, reflecting the diverse processes contributing to CH₄ emissions in agriculture. The first three components were estimated from the integration of data from EDGAR v8 (Crippa et al., 2023), CEDS v2024_04_01 (Hoesly & Smith, 2024), and Statistical Database of the Food and Agriculture Organization of the United Nations (FAOSTAT 2023, Tubiello et al., 2021, 2022). The CH₄ emissions from agricultural waste burning were synthesized utilizing values from EDGAR v8 (the large-scale agricultural waste burning included in EDGAR is sourced from the Global Wildfire Information System (GWIS, https://gwis.jrc.ec.europa.eu/); Crippa et al., 2023), FAOSTAT 2023 (Tubiello et al., 2021, 2022), and fourth version of the Global Fire Emissions Database (GFED v4.1s, Van Der Werf et al., 2017).

For the waste sector, CH₄ emissions were accounted for from the solid waste disposal (e.g., landfills), biological treatment of solid waste, incineration and open burning of waste, and wastewater treatment and discharge. The estimations were derived from the synthesis of EDGAR v8 (Crippa et al., 2023) and CEDS v2024_04_01 (Hoesly & Smith, 2024).

### 2.4.1.2. Natural Sources and Sinks of CH₄

CH₄ emissions from natural wetlands have been identified as a significant natural source of CH₄ (Saunois et al., 2024). We utilized 11 TBMs' simulated results from the Global Methane Budget (GMB, Saunois et al., 2024) project for regional and national estimates at annual steps for the period from 2000 to 2020. The ensemble means were calculated at the original resolution of each model. For mapping purposes, we integrated the gridded fluxes to a standardized resolution of 0.5° × 0.5° by applying the nearest neighbor interpolation to models with original resolutions coarser than 0.5° × 0.5°.

The CH₄ emissions from wildfires were obtained from GFED v4.1s (Van Der Werf et al., 2017). GFED v4.1s contained burned area data, dry-matter mass for combustion, and EFs used to convert dry matter burned into emissions of various trace gases and aerosols for both agricultural waste burning and different types of wildfires, including tropical forest fires, temperate forest fires, boreal forest fires, and peat fires. We included the CH₄ emissions from agricultural waste burning in GFED v4.1s for agricultural waste burning emissions (Section 2.4.1.1) and the combined CH₄ emissions from savanna fires, temperate forest fires, and boreal forest fires for wildfire emissions, as these were the wildfire types indicated in the CWA region by the data set.

We derived the amount of CH₄ emissions from inland waters using the data set from Lauerwald et al. (2023). Four types of inland water bodies were considered in the CH₄ emissions estimation: rivers, lakes, reservoirs, and lakes regulated by dams. For each type of water body, 1, 6, 8, and 6 literature estimates were used in the data regionalization and synthesis, respectively (Supporting Information S3 of Lauerwald et al., 2023). The estimation of regional and national CH₄ emissions used the same procedure as for calculating CO₂ emissions from inland waters.

Significant amounts of CH₄ produced within the Earth's crust and naturally released to the atmosphere have been recognized and defined as geological CH₄ seepage (Etiope et al., 2019). We derived the amount of geological CH₄ emissions from the gridded maps of geological CH₄ emissions developed by Etiope et al. (2019). The data set contained CH₄ emissions from four types of geological sources: onshore hydrocarbon macro-seeps, submarine (offshore) seeps, diffuse microseepage, and geothermal manifestations. We assumed the geological CH₄ emissions to be time-invariant, without interannual variations.

### 2.4.2. Top-Down Approach

Five atmospheric inversion systems from the GMB were used to estimate the TD approached CH₄ budget for the CWA region during 2000–2020 (Saunois et al., 2024). The GMB inversion systems were derived from either in situ (surface) observations of atmospheric CH₄ mole fractions or satellite-derived CH₄ column-averaged mole fractions (Saunois et al., 2024). This approach resulted in the inversion estimates with two distinct time coverages. Surface-based inversions generally provided estimates from 2000 to 2020. Satellite-based inversions, primarily
using data from the Greenhouse Gases Observing Satellite (GOSAT), covered 2010 to 2020 due to the availability of GOSAT-based CH₄ measurements starting in 2009 (GOSAT Project, 2011). To ensure consistency in temporal and spatial coverage between the TD and BU approaches for the CH₄ budget, inversion estimates based on surface stations were adopted in this study. Consequently, five inversion systems were adopted: the Community Inversion Framework with the Laboratoire de Météorologie Dynamique zoom atmospheric general circulation model (LMDz-CIF, Thanwerdas et al., 2022), the Carbon Tracker-Europe CH₄ (CTE-CH₄, Tsuruta et al., 2017), the Model for Interdisciplinary Research on Climate version 4.0-based Atmospheric Chemistry and Transport Model (MIROC4-ACTM, Patra et al., 2016, 2018; Chandra et al., 2021), the National Institute for Environmental Studies Transport Model with the FLEXible PARTicle dispersion model (NIES-TM-FLEXPART, Maksyutov et al., 2021; F. Wang et al., 2019), and the Nonhydrostatic Icosahedral Atmospheric Model-based Inversion Simulation for Monitoring CH₄ (NISMON-CH₄, Niwa, Fujii, et al., 2017; Niwa, Tomita, et al., 2017).

The inversion systems utilized atmospheric CH₄ observations from multiple networks and stations worldwide. Specifically, LMDz-CIF, CTE-CH₄, and MIROC4-ACTM used CH₄ observations from the National Oceanic and Atmospheric Administration (NOAA) flask network (Lan et al., 2024; Steele et al., 1987) and the Commonwealth Scientific and Industrial Research Organisation (CSIRO, Francey et al., 1999). LMDz-CIF and CTE-CH₄ also incorporated CH₄ measurements from the Advanced Global Atmospheric Gases Experiment (AGAGE, Cunnold et al., 2002; Prinn et al., 2018) and in situ and flask measurements from individual stations operated by the National Institute for Environmental Studies (NIES), the Finnish Meteorological Institute (FMI), the Laboratoire des Sciences du Climat et de l'Environnement (LSCE), and Environment Canada (EC) (Saunois et al., 2024). Both NIES-TM-FLEXPART and NISMON-CH₄ included surface observations from the CH₄ GLOBALVIEW plus v4.0 ObsPack (Schuldt, Aalto, et al., 2021) and the CH₄ NRT v4.0 ObsPack (Schuldt, Jacobson, et al., 2021). Additionally, NIES-TM-FLEXPART included in situ and flask measurements from the Integrated Carbon Observation System (ICOS) in Europe (Saunois et al., 2024). NISMON-CH₄ included observations from the NIES (Saunois et al., 2024).

The ensemble mean of the five inversion systems' estimates was calculated to assess the TD approached CH₄ budgets. Each inversion system provided one or more simulation results depending on the hydroxyl radical inter-annual variability considered and the prior fluxes used (Saunois et al., 2024). For these multiple estimates from the same inversion system, the regional estimates were first averaged to yield a consolidated estimate for each inversion at its original resolution, which was then combined with estimates from other inversions to calculate the ensemble mean and uncertainties. For mapping purposes, the nearest neighbor interpolation was applied to integrate the fluxes from the original model resolution to a 1° × 1° resolution.

## 2.5. N₂O Budget

### 2.5.1. Bottom-Up Approach

The accounting framework of the N₂O BU budget was similar to that used for the CO₂ BU budgets, encompassing both anthropogenic and natural sources of N₂O emissions. The anthropogenic N₂O emissions were categorized into four sectors: energy, industry, agriculture, and waste. The natural sources of N₂O emissions included three components from two origins: soil emissions and wildfire-induced emissions from terrestrial natural ecosystems, and emissions from inland water bodies. The coverage and estimation procedures for each sector are described below, with more detailed information available in Table S3 in Supporting Information S1.

#### 2.5.1.1. Anthropogenic Emissions of N₂O

For the energy sector, the sources of N₂O emissions included were identical to those of CO₂ and CH₄ emissions, with estimations derived from the synthesis of EDGAR v8 (Crippa et al., 2023) and CEDS v2024_04_01 (Hoesly & Smith, 2024).

For the industry sector, the N₂O budgets included emissions from the combustion of fuel during (a) industrial processes, (b) chemical industry (e.g., ammonia, urea, and undesired byproducts of nitric and adipic acid production), and (c) evaporation and emission from various product uses, such as propellants in aerosol products, oxidizing agents, and etchants used in semiconductor manufacturing and medical applications. The estimations for the first two sources were obtained from the synthesis of EDGAR v8 (Crippa et al., 2023) and CEDS
v2024_04_01 (Hoesly & Smith, 2024), while the last source was only available from EDGAR v8 (Crippa et al., 2023).

Agricultural N₂O emissions have been identified as the largest source of anthropogenic N₂O emissions, repre-
senting 58.2% of the global total anthropogenic N₂O emissions in 2020 (Tian et al., 2024). For N₂O emissions
from the agricultural sector in CWA, five emitting pathways and sources were accounted for: direct N₂O
emissions from manure management, direct N₂O emissions from managed soils, indirect N₂O emissions from
manure management, indirect N₂O emissions from managed soils, and direct N₂O emissions from agricultural
waste burning. The estimations for the first four components of the agricultural budget were derived from the
synthesis of EDGAR v8 (Crippa et al., 2023), CEDS v2024_04_01 (Hoesly & Smith, 2024), and FAOSTAT 2023
(Tubiello et al., 2021, 2022). The estimation for the last component was derived from the synthesis of EDGAR v8
(Crippa et al., 2023), FAOSTAT 2023 (Tubiello et al., 2021, 2022), and GFED v4.1s (Van Der Werf et al., 2017).

For the waste sector, sources of N₂O emissions included the biological treatment of solid waste, incineration and open burning of waste, and wastewater treatment and discharge. The estimates of these components were derived from the synthesis of EDGAR v8 (Crippa et al., 2023) and CEDS v2024_04_01 (Hoesly & Smith, 2024).

2.5.1.2. *Natural Sources of N₂O*

Soil N₂O emission from terrestrial natural ecosystems were calculated as the ensemble mean of eight TBMs that participated in the Global N₂O Model Intercomparison Project phase-2 (NMIP2, Tian et al., 2018, 2024). These models simulate key processes influencing N₂O production and emission, such as autotrophic nitrification, denitrification, plant nitrogen (N) uptake, ammonia volatilization, nitrate leaching, and soil thermal and hydro- logical processes (Tian et al., 2018). In each pixel of the gridded fluxes, there are both natural and agricultural soils. Anthropogenic exogenous N addition to cropland, pasture, and rangeland constitutes an important source of agricultural soil N₂O emissions (Dangal et al., 2019; Tian et al., 2024). To ensure the emissions are solely from natural ecosystems, we selected and processed the outputs of each model differently based on the exogenous N processes included in the model and the availability of outputs from factorial experiments. For models consid- ering the impacts of anthropogenic N input in cropland, pasture, and rangeland, we first acquired the gridded-level results of scenario SH12 of NMIP2. In this scenario, all models were driven by time-varying environmental factors like land use change, climate change, and changes in atmospheric CO₂ concentration, while keeping management practices constant at the level of 1850. This approach enabled the simulation of present-day emissions without the impacts of farming practices such as N fertilizer application, manure application, and irrigation. We then adjusted the estimates from SH12 by subtracting the difference in cropland grid cells between the “all-combined” scenario results (SH1) and the atmospheric N deposition run (SH4) of the factorial experi- ments to remove the N deposition-induced N₂O emissions in croplands from the gridded fluxes (Tian et al., 2018, 2024). Thus, the final estimates represented only the emissions from soils in absence of anthropogenic N inputs and were used to reflect N₂O emissions from natural soils. For models that consider the impacts of anthropogenic N input in croplands and indicate all grasslands as natural ecosystems, we first acquired the gridded-level results of the “all-combined” scenario (SH1). We then quantified the contribution of each pathway of N addition on cropland soil N₂O emissions by calculating the difference between cropland SH1 results and the cropland results from factorial experiments (N deposition: SH1-SH4, N fertilizer: SH1-SH3, Manure N: SH1-SH2). Finally, grid- level SH1 was adjusted by subtracting the sum of all anthropogenic N-induced cropland soil N₂O emissions, resulting in estimates that represent only naturally occurring soil N₂O emissions, unaffected by anthropogenic N inputs. After processing each model's soil N₂O emission outputs accordingly, we averaged the results from all models to estimate natural soil N₂O emissions. More details about the factorial experiment design can be found in Tian et al. (2024). Additionally, the N₂O emissions from managed soils caused by atmospheric N deposition, N fertilizer, and manure application have been accounted for in the agricultural sector of anthropogenic N₂O emissions (Section 2.5.1.1).

We used GFED v4.1s (Van Der Werf et al., 2017) to estimate N₂O emissions from wildfires. Similar to the estimation of CH₄ emissions from wildfires, emissions from three wildfire types (savanna fires, temperate forest fires, and boreal forest fires) were included.

For N₂O emissions from inland waters, we used the data set from Lauerwald et al. (2023). N₂O emissions from four types of water bodies were included: rivers, lakes, reservoirs, and lakes regulated by dams. For each water type, data from 3, 3, 6, and 5 literature studies were synthesized, respectively (Supporting Information S3 of
Lauerwald et al., 2023). The estimation of regional and national N₂O emissions used the same procedure as for calculating CO₂ and CH₄ emissions from inland waters.

### 2.5.2. Top-Down Approach

Four atmospheric inversion frameworks from the Global Nitrous Oxide Budget (GNB) project (Tian et al., 2024) were utilized to estimate the TD approached budgets for CWA during 2000–2020. The selection was consistent with the criteria for selecting inversion results for the TD CO₂ budget. The inversion frameworks, namely INVICAT, PyVAR-CAMS, MIROC4-ACTM, and GEOS-Chem, are described as follows: INVICAT (Wilson et al., 2014), which employs the TOMCAT transport model driven by the European Center for Medium-Range Weather Forecasts' (ECMWF) fifth generation of global atmospheric reanalysis, known as ECMWF ERA5 (Hersbach et al., 2020); PyVAR-CAMS (Thompson et al., 2014), which uses the LMDz5 transport model, also driven by ECMWF ERA5 meteorology; MIROC4-ACTM (Patra et al., 2018, 2022), which utilizes the ACTM transport model driven by the Japanese 55-year Reanalysis (JRA-55, Kobayashi et al., 2015; Harada et al., 2016) meteorology; and GEOS-Chem (Wells et al., 2018), which relies on its identically named transport model driven by the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2, Gelaro et al., 2017) meteorology. These frameworks apply Bayesian inversion methods, extensively detailed in the Supporting Information provided by Tian et al. (2024).

All inversions used atmospheric N₂O observations from the NOAA Global Monitoring Laboratory (NOAA/GML, Dutton et al., 2024). Additionally, INVICAT, PyVAR-CAMS and GEOS-Chem used N₂O observations from the AGAGE data set (Prinn et al., 2022), the CSIRO flask network (Francey et al., 2003), as well as measurements from individual stations operated by NIES, FMI, the University of Edinburgh (UE), and the JMA (Tian et al., 2024).

For the CWA region TD N₂O budget, we calculated the mean posterior estimates from the four atmospheric inversions at their original resolutions. For mapping purposes, the nearest neighbor interpolation was applied to integrate the gridded fluxes at a 1° × 1° resolution, ensuring uniformity across all data sets.

## 2.6. Calculation of Synthetic GHG Budgets

The total budgets of CO₂, CH₄, and N₂O at an annual step from 2000 to 2020 were calculated independently using BU and TD approaches. To compute the total combined budgets, Global Warming Potentials (GWP) over 100-year (GWP100) and 20-year (GWP20) time horizons were used. GWP is not a direct radiative forcing-based estimate but an emission-based estimate that compares the cumulative radiative forcing from the emission of a unit mass of a given gas over a specified time horizon, relative to that from the emission of the same mass of CO₂, which is used as the reference gas (IPCC, 2021). This comparison yields dimensionless factors (the GWP values) that reflect the relative impact of each gas on global warming. Therefore, by multiplying the emissions of CH₄ and N₂O by their respective GWP values, we can convert their impacts into CO₂-equivalent units (unit abbreviation CO₂eq). This allows us to sum the CO₂ emissions and the CO₂-equivalent emissions of CH₄ and N₂O to obtain a total GHG budget, enabling comparison of relative contributions across gases and sectors. In this study, the combined budget of these three GHGs was referred to as the GHG budget. The GWP100 values used were 29.8 for CH₄ from fossil fuel sources, 27.0 for CH₄ from other sources, and 273 for N₂O. The GWP20 values used were 82.5 for CH₄ from fossil fuel sources, 79.7 for CH₄ from other sources, and 273 for N₂O. These values are referenced in IPCC AR6 Table 7.15 (Forster et al., 2021). More details on combining equations and GWP values are available in Text S1 and Table S4 in Supporting Information S1.

## 2.7. Data Analysis and Uncertainty Estimate

We focused on the annual estimates, decadal means, and changing rates of CO₂, CH₄, N₂O, and their combined GHG for the period from 2000 to 2020. We reported the annual budgets and their components in teragrams of the corresponding gas per year (e.g., Tg CO₂ yr⁻¹, Tg CH₄ yr⁻¹, Tg N₂O yr⁻¹, Tg CO₂eq yr⁻¹). Negative values in emissions or fluxes represented removal from the atmosphere (a sink), while positive values indicated an addition to the atmosphere (a source). This methodology was consistent with the approaches adopted in the GCB project (Friedlingstein et al., 2022), GMB project (Saunois et al., 2024), and GNB project (Tian et al., 2024). The decadal means for the 2000s (2000–2009) and 2010s (2010–2019) were calculated as the average of the annual values for each decade, also expressed in teragrams of the corresponding gas per year. The temporal changing rates from
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Annual anthropogenic CO₂ emissions by sectors for (a) CWA region during 2000–2020 and for (b) CWA region and countries in the 2010s. The yellow, blue, and green colors represent emissions from the energy, industry and agriculture sectors, respectively. CO₂ emissions from the waste sector are significantly lower than from other sectors and are not depicted in this figure. The full names of the country abbreviations in (b) can be found in Section 2.1 of the Methods.</figcaption></figure>

2000 to 2020 for each gas were calculated as the first derivative of the annual budgets using linear regression. These temporal changing rates were expressed in teragrams of the gas per year squared (e.g., Tg CO₂ yr⁻², Tg CH₄ yr⁻², Tg N₂O yr⁻²).

The uncertainties for each component of each GHG were calculated using different methods, depending on the number of data sources available. For components with multiple data sources, uncertainty was quantified as the standard deviation between individual estimates. For components where only a single data source was available and the original data included an uncertainty, we adopted that reported uncertainty. When no uncertainty was reported for components with a single data source, we utilized the 95% confidence interval uncertainty proportion reported by the respective data set to calculate the uncertainty of that specific term. For example, the uncertainty of CO₂ emissions from glass production was calculated using the statistical uncertainty in activity data from Solazzo et al. (2021). Similarly, the uncertainty of geological CH₄ emissions was quantified using the statistical uncertainty reported by Etiope et al. (2019), and the wildfire emissions were examined using the statistical uncertainty reported by Van Der Werf et al. (2017). The uncertainties in the total budget of each gas and the synthetic GHG were calculated by employing error propagation to combine the uncertainties associated with each gas and each component. For the uncertainty that was quantified using the 95% confidence interval uncertainty proportion, it was first divided by a conversion factor of 1.96 (Frey et al., 2006), then combined with other standard deviations using the error propagation formula. Unless specified, uncertainties are reported in parentheses as minimum and maximum values of all estimates, following Tian et al. (2020). More details on uncertainty estimates are available in Text S2 and Table S5 in Supporting Information S1.

# 3. Results

## 3.1. CO₂ Budget

### 3.1.1. Bottom-Up Estimates

#### 3.1.1.1. Anthropogenic CO₂ Emissions

The total anthropogenic CO₂ emission in CWA showed a very large increase in the last two decades, from 1,615.9 (1,590.0–1,641.7) Tg CO₂ yr⁻¹ in 2000 to 2,958.1 (2,922.1–2,994.2) Tg CO₂ yr⁻¹ in 2020, at a mean rate of 76.9 Tg CO₂ yr⁻² (*p* < 0.05; Figure 3a). Among all anthropogenic sources, the energy sector was the most significant contributor, with CO₂ emissions starting at 1,244.7 (1,220.4–1,269.0) Tg CO₂ yr⁻¹ in 2000 to 2,203.0 (2,172.2–2,233.8) Tg CO₂ yr⁻¹ in 2020. The energy sector showed a continuous increase until 2019, followed by a slight decline in 2020, due to COVID. This sector exhibited the fastest increase rate of 55.2 Tg CO₂ yr⁻² (*p* < 0.05) and accounted for the largest proportion of the CWA anthropogenic CO₂ emissions' increase (71.4%) during 2000–2020. Emissions caused by electricity and heat generation was the leading source (49.6%) of the sector's increase, followed by road transportation (25.1%), building energy (14.7%), and petroleum refining
<table><caption>**Table 2**
CO₂ Budget in Central and West Asia for the 2000s, 2010s, and the Year 2020</caption><thead><tr><th rowspan="2">Sectors</th><th rowspan="2"></th><th colspan="3">CO₂ (Tg CO₂ yr⁻¹)</th></tr><tr><th>2000–2009<br>Mean (min, max)</th><th>2010–2019<br>Mean (min, max)</th><th>2020<br>Mean (min, max)</th></tr></thead><tbody><tr><td rowspan="5">Anthropogenic Emissions</td><td>Energy</td><td>1517.0 (1492.6, 1541.4)</td><td>2104.6 (2078.0, 2131.1)</td><td>2203.0 (2172.2, 2233.8)</td></tr><tr><td>Industry</td><td>459.7 (449.9, 469.6)</td><td>703.8 (691.0, 716.6)</td><td>744.5 (725.7, 763.2)</td></tr><tr><td>Agriculture</td><td>6.5 (6.0, 7.0)</td><td>8.0 (7.4, 8.6)</td><td>9.1 (8.5, 9.8)</td></tr><tr><td>Waste</td><td>1.3 (1.1, 1.6)</td><td>1.5 (1.2, 2.0)</td><td>1.6 (1.3, 2.0)</td></tr><tr><td>Subtotal</td><td>1984.5 (1958.2, 2010.8)</td><td>2817.9 (2788.5, 2847.4)</td><td>2958.1 (2922.1, 2994.2)</td></tr><tr><td rowspan="3">Natural Sinks and Sources</td><td>NBP</td><td>-173.0 (-249.1, -97.0)</td><td>-249.6 (-343.5, -155.7)</td><td>-248.3 (-392.3, -104.4)</td></tr><tr><td>Inland waters</td><td>73.0 (61.7, 99.4)</td><td>73.0 (61.7, 99.4)</td><td>73.0 (61.7, 99.4)</td></tr><tr><td>Subtotal</td><td>-100.0 (-176.9, -19.5)</td><td>-176.6 (-271.2, -79.1)</td><td>-175.3 (-319.8, -29.0)</td></tr><tr><td rowspan="2">Net Balance</td><td>BU approach</td><td>1884.5 (1803.2, 1969.2)</td><td>2641.3 (2542.2, 2743.2)</td><td>2782.8 (2634.0, 2933.5)</td></tr><tr><td>TD approach</td><td>1968.7 (1630.0, 2307.4)</td><td>2974.4 (2457.9, 3490.8)</td><td>3277.5 (2818.7, 3736.3)</td></tr></tbody></table>

(10.4%; Table S1 in Supporting Information S2). Emissions from the industry sector also increased from 364.1 (355.5–372.8) Tg CO₂ yr⁻¹ in 2000 to 744.5 (725.7–763.2) Tg CO₂ yr⁻¹ in 2020 (Figure 3a). The mean growth rate was 21.5 Tg CO₂ yr⁻² (*p* < 0.05), primarily driven by combustion of fuels in manufacturing industries and construction (49.4%), chemical industry (23.3%), and cement production (22.9%, Table S1 in Supporting Information S2). The agricultural sector exhibited a more modest increase in emissions with a smaller magnitude, starting from 5.9 (5.5–6.3) Tg CO₂ yr⁻¹ in 2000 and reaching 9.1 (8.5–9.8) Tg CO₂ yr⁻¹ by 2020, at a gradual rate of 0.2 Tg CO₂ yr⁻² (*p* < 0.05, Figure 3a). Urea application and liming contributed 55.4% and 44.6%, respectively, to the increase in agricultural emissions (Table S1 in Supporting Information S2). Emissions from the waste sector, although small, accounting for less than 1% of total anthropogenic emissions, increased from 1.1 (0.9–1.4) Tg CO₂ yr⁻¹ in 2000 to 1.6 (1.3–2.0) Tg CO₂ yr⁻¹ in 2020 at the rate of 2.4 × 10⁻² Tg CO₂ yr⁻² (*p* < 0.05).

For the decade 2010–2019, the CWA total anthropogenic CO₂ emissions were 2,817.9 (2,788.5–2,847.4) Tg CO₂ yr⁻¹ (Table 2), accounting for 7.1% of the global total anthropogenic CO₂ emissions (39,699.1 Tg CO₂ yr⁻¹ including the cement carbonation sink (Friedlingstein et al., 2023)) during the same period. Countries within CWA showed large differences in the magnitude of emissions, and the top 10 countries contributed to 88.1% of the regional anthropogenic CO₂ emissions (Figure 3b). Iran was the highest CO₂ emitter with an average of 621.3 (606.2–636.5) Tg CO₂ yr⁻¹ in the 2010s, followed by Saudi Arabia (562.7, 549.1–576.4 Tg CO₂ yr⁻¹), Turkey (372.9, 355.9–389.9 Tg CO₂ yr⁻¹), Kazakhstan (224.1, 215.7–232.5 Tg CO₂ yr⁻¹), the United Arab Emirates (189.3, 184.4–194.1 Tg CO₂ yr⁻¹), Iraq (144.4, 141.0–147.9 Tg CO₂ yr⁻¹), Uzbekistan (113.8, 111.3–116.2 Tg CO₂ yr⁻¹), Kuwait (92.7, 91.2–94.1 Tg CO₂ yr⁻¹), Qatar (90.8, 89.4–92.3 Tg CO₂ yr⁻¹), and Oman (70.4, 69.0–71.7 Tg CO₂ yr⁻¹) respectively. Among these countries, with the exception of Qatar and the United Arab Emirates, electricity and heat generation activities were the primary sources of CO₂ emissions, contributing 21.6%–48.7% of their decadal means in the 2010s. Petroleum refining was the main source of Qatar's CO₂ emissions, accounting for 32.8% of its decadal average. Manufacturing industries and construction were the main sources of CO₂ emissions in the United Arab Emirates, accounting for 37.0% of the anthropogenic total in the 2010s.

### 3.1.1.2. Natural Sinks and Sources of CO₂

During 2000–2020, the ensemble mean NBP of the natural ecosystem in the CWA region was −213.1 (−311.8 to −114.4) Tg CO₂ yr⁻¹, fluctuating between −658.7 and 326.6 Tg CO₂ yr⁻¹ as derived from 15 TRENDY TBMs (Figure 4a). The 15 TBMs exhibited divergent interannual variability, with none indicating a significant trend in NBP over the last two decades (Figure S1a in Supporting Information S1).

In the 2010s, the regional NBP was −249.6 (−343.5 to −155.7) Tg CO₂ yr⁻¹, accounting for 4.2% of the global net terrestrial CO₂ sink (5,925.6 Tg CO₂ yr⁻¹ including land-use change emissions (Friedlingstein et al., 2023)) during the same period. Kazakhstan and Turkey made the largest contributions to the regional NBP, with a
<figure><img src="image_6.png" /></figure>

**Figure 4.** Temporal dynamics and spatial variations of natural sinks and sources of CO₂ in the CWA Region. (a) Interannual variability of net biome productivity (NBP, green) and CO₂ emissions from rivers (light blue), natural lakes (sky blue), reservoirs (medium blue), and lakes regulated by dams (dark blue). (b) Decadal mean CO₂ fluxes at national and regional levels for the 2010s. (c) Spatial distribution of ensemble NBP rates in the 2010s from TRENDY models. (d) Spatial distribution of uncertainty (one standard deviation between individual models) in NBP rate estimates from TRENDY models during the 2010s. In (a–c), positive values indicate CO₂ release to the atmosphere, while negative values represent CO₂ uptake by land. In (a, b), the colors within each bar indicate the contributions of individual source categories to the total flux each year or each country.

decadal mean of −76.9 (−152.7 to −1.2) Tg CO₂ yr⁻¹ and −72.7 (−111.4 to −34.0) Tg CO₂ yr⁻¹, accounting for 30.8% and 29.1% of the total, respectively (Figure 4b). Figures 4c and 4d displays the spatial distribution of the ensemble NBP rates and associated uncertainty in the 2010s. The averaged NBP rate across the CWA region was −20.4 g CO₂ m⁻² yr⁻¹. Several countries within the CWA region, including Georgia, Turkey, Cyprus, Kyrgyzstan, Kazakhstan, and Tajikistan, exhibited comparatively stronger C sequestration, with NBP rates of −101.5, −85.3, −53.7, −35.9, −26.1 and −24.2 g CO₂ m⁻² yr⁻¹, respectively (Figure 4c). These can be attributed to the presence of forests, shrublands, and grasslands in these regions as indicated by the LUH2v2 data set (Hurtt et al., 2011), which used as the primary land cover driving force for TRENDY models (Friedlingstein et al., 2022). Other countries in the region showed relatively lower NBP rates, ranging from −20.2 to −2.8 g CO₂ m⁻² yr⁻¹. Notably, Kuwait, Oman, and Azerbaijan were small biospheric C sources, displaying mean NBP values of 1.3, 5.3, and 6.5 g CO₂ m⁻² yr⁻¹, respectively. Larger uncertainties were mainly found in areas with higher NBP rates, such as the northwest of Turkey, Georgia, northeast Kazakhstan, and northeast Kyrgyzstan (Figure 4d). Additionally, obvious uncertainties were found in certain arid regions, including Turkmenistan, southern Afghanistan, central and southwest Saudi Arabia, and western Yemen. This may be due to slight differences in land-cover maps between models. In TRENDY, each modeling team decides on the land cover classification scheme that best fits their TBM, with a recommendation to use spatially explicit transitions from LUH2v2 (Ciais et al., 2022).

For inland water CO₂ emissions, the regional total was 73.0 (61.7–99.4) Tg CO₂ yr⁻¹, with 33.0 (29.0–49.1) Tg CO₂ yr⁻¹ from rivers, 34.0 (23.6–54.9) Tg CO₂ yr⁻¹ from natural lakes, 5.0 (3.8–5.8) Tg CO₂ yr⁻¹ from reservoirs, and 1.0 (1.0–2.5) Tg CO₂ yr⁻¹ from lakes regulated by dams (Figure 4a, Table 2). This represents 1.3% of the global total inland water CO₂ emissions (5,548 Tg CO₂ yr⁻¹ (Lauerwald et al., 2023)). Kazakhstan had the highest CO₂ emissions from inland waters, accounting for 39.7% of the region's total emissions, contributing 40.2% (13.3, 9.7–27.7 Tg CO₂ yr⁻¹), 41.0% (13.9, 5.3–31.2 Tg CO₂ yr⁻¹), 21.6% (1.1, 0.4–1.5 Tg CO₂ yr⁻¹), and 71.2% (0.7, 0.7–2.1 Tg CO₂ yr⁻¹) of the total emissions from rivers, lakes, reservoirs, and lakes regulated by dams, respectively. This large contribution is related to the surface area, which represents 40.6% (100,123.3 km²)
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Temporal changes and decadal average of CO₂ budgets in the CWA region. (a) Comparison between the regional bottom-up (BU, orange) CO₂ total budget and the top-down (TD, blue) CO₂ budget during 2000–2020. Shaded area represents the uncertainty ranges. (b) BU anthropogenic (BU ant, gray), BU natural (BU nat, green), and BU total (BU tot, black dots) CO₂ budgets for each CWA country and the CWA region in the decade 2010–2019 (2010s). Error bars indicate the uncertainty ranges.</figcaption></figure>

of the regional total surface area of the inland water bodies (246,788.4 km²). Followed by Uzbekistan and Turkmenistan, which had inland water CO₂ emissions of 9.0 (4.7–17.6) and 7.0 (3.4–14.4) Tg CO₂ yr⁻¹, respectively, accounting for 12.3% and 9.6% of the regional inland water CO₂ emissions.

### 3.1.2. CO₂ Budget Synthesis

During 2000–2020, the CWA BU CO₂ budget increased from 1,702.2 (1,519.5–1,886.4) Tg CO₂ yr⁻¹ to 2,782.8 (2,634.0–2,933.5) Tg CO₂ yr⁻¹ at a rate of 71.8 Tg CO₂ yr⁻² (*p* < 0.05), and the TD CO₂ budget increased from 1,314.0 (359.8–2,268.2) to 3,277.5 (2,818.7–3,736.3) Tg CO₂ yr⁻¹ at a rate of 89.5 Tg CO₂ yr⁻² (*p* < 0.05) (Figure 5a). The decadal means for the 2000s and 2010s, and for the year 2020 are shown in Table 2. Both BU and TD approaches indicated that the CWA region was a net C source, meaning that CO₂ emissions from human activities far exceeded the C absorbed by natural ecosystems, which could offset only about 5.0%–6.3% of these emissions based on the BU results (Table 2). Overall, the two approaches show a nice agreement in interannual variability, possibly because water availability, a key factor in the CWA region, is easier for models to capture. However, the difference in magnitude between the two has been gradually widening since 2009 (Figure 5a). This can be primarily attributed to differences in fossil fuel emissions data. In the BU approach, fossil fuel emissions are based on the latest national reports and international inventories (e.g., EDGAR v8, CEDS v2024_04_01), whereas in the TD approach, these emissions are typically prescribed (e.g., GCP fossil fuel CO₂ emissions are from EDGAR v4.3.2 (Friedlingstein et al., 2022; Janssens-Maenhout et al., 2019)). As fossil fuel combustion is the predominant source of CO₂ emissions in the CWA region (Figures 3a and 3b), the inaccuracies or inconsistencies in the inventory data sets can lead to increasing discrepancies between BU and TD estimates, so the results from inversions may not be quite strong.

For the decade 2010–2019, the total BU CO₂ budget of CWA was 2,641.3 (2,542.2–2,743.2) Tg CO₂ yr⁻¹. Anthropogenic and natural budgets were estimated to be 2,817.9 (2,788.5–2,847.4) and –176.6 (–271.2 to –79.1) Tg CO₂ yr⁻¹, respectively (Figure 5b; Table 2). Terrestrial natural ecosystems (including inland waters) were net CO₂ sinks that offset about 6.3% of emissions from anthropogenic sources. At the national level, Iran (BU total: 606.2 (579.0–633.5) Tg CO₂ yr⁻¹) exhibited the largest net CO₂ sources in the CWA region, closely followed by Saudi Arabia (BU total: 555.4 (532.5–578.4) Tg CO₂ yr⁻¹). Their land CO₂ sinks offset about 2.4% and 1.3% of their anthropogenic emissions, respectively. Turkey and Kazakhstan exhibited relatively strong natural CO₂ sinks of –67.8 (–106.5 to –28.9) Tg CO₂ yr⁻¹ and –47.9 (–124.3 to 31.1) Tg CO₂ yr⁻¹, respectively, as compared to other countries within this region (Table S1 in Supporting Information S2). However, these could only offset 18.2% and 21.4% of their anthropogenic emissions, much lower than the global average of 30.6% (Friedlingstein et al., 2022). This implies the limited capacity of natural sinks to mitigate the anthropogenic emissions and emphasizes the need for effective CO₂ emission reduction strategies to achieve C neutrality in the region.
<figure><img src="image_8.png" /><figcaption>**Figure 6.** Annual anthropogenic CH₄ emissions by sectors for (a) CWA region during 2000–2020 and for (b) CWA region and countries in the 2010s. The yellow, blue, green and brown colors represent emissions from the energy, industry, agriculture and waste sectors, respectively.</figcaption></figure>

## 3.2. CH₄ Budget

### 3.2.1. Bottom-Up Approach

#### 3.2.1.1. Anthropogenic CH₄ Emissions

Total anthropogenic CH₄ emissions from CWA showed a steady increase over the last two decades, from 25.3 (24.2–26.5) Tg CH₄ yr⁻¹ in 2000 to 37.2 (35.7–38.7) Tg CH₄ yr⁻¹ by 2020, at a mean rate of 0.7 Tg CH₄ yr⁻² (*p* < 0.05; Figure 6a). The energy sector showed a continuous upward trend, with emissions expanding from 17.2 (16.1–18.3) Tg CH₄ yr⁻¹ to 23.1 (21.7–24.5) Tg CH₄ yr⁻¹, with an annual growth rate of 0.4 Tg CH₄ yr⁻² (*p* < 0.05). The increase is dominated by fugitive emissions from the extraction and production of oil and natural gas (93.1%) (Figure 6a, Table S2 in Supporting Information S2). Although low in magnitude, CH₄ emissions from the industrial sector displayed a steady increase, rising from 2.8 × 10⁻² (2.7 × 10⁻² to 2.9 × 10⁻²) Tg CH₄ yr⁻¹ in 2000 to 9.0 × 10⁻² (8.4 × 10⁻² to 9.6 × 10⁻²) Tg CH₄ yr⁻¹ in 2020. The sector experienced an annual growth rate of 3.2 × 10⁻³ Tg CH₄ yr⁻² (*p* < 0.05), with the chemical industry making a significant contribution (80.8%) to this growth. The agriculture sector showed a continuous rise in emissions from 4.1 (4.0–4.3) Tg CH₄ yr⁻¹ to 6.1 (5.7–6.5) Tg CH₄ yr⁻¹, at a rate of 0.1 Tg CH₄ yr⁻² (*p* < 0.05), primarily driven by livestock enteric fermentation, which accounted for 91.7% of the total increase in the sector. The waste sector also exhibited sustained growth at a rate of 0.2 Tg CH₄ yr⁻² (*p* < 0.05), nearly doubling its emissions from 4.0 (3.9–4.1) Tg CH₄ yr⁻¹ in 2000 to 7.9 (7.7–8.0) Tg CH₄ yr⁻¹ in 2020. This increase was mainly attributed to solid waste disposal and wastewater treatment and discharge, which contributed 64.3% and 33.4% of the sector's total increase, respectively.

In the 2010s, anthropogenic CH₄ emissions in the CWA were 34.8 (33.2–36.4) Tg CH₄ yr⁻¹ (Figure 6b, Table 3), accounting for 9.7% of the global total direct anthropogenic emissions (358 Tg CH₄ yr⁻¹ (Saunois et al., 2024)) during the same period. Fugitive emissions from oil and gas were the dominant components, accounting for 60.3% of the CWA region's anthropogenic emissions during this period and representing 52.5% of the global total fugitive emissions from oil and natural gas during the same time frame (40 Tg CH₄ yr⁻¹ (Saunois et al., 2024)). For the decade 2010s, anthropogenic emissions varied among the CWA countries, with the top 10 emitting countries contributing 82.4% of the regional emissions. Their national anthropogenic CH₄ emissions ranked in decreasing order were Iran (6.2, 5.3–7.1 Tg CH₄ yr⁻¹), Iraq (4.37, 4.30–4.44 Tg CH₄ yr⁻¹), Saudi Arabia (3.9, 3.8–4.0 Tg CH₄ yr⁻¹), Turkey (3.5, 3.2–3.7 Tg CH₄ yr⁻¹), Qatar (2.2, 1.5–3.0 Tg CH₄ yr⁻¹), Kazakhstan (2.2, 2.0–2.4 Tg CH₄ yr⁻¹), Uzbekistan (1.9, 1.2–2.6 Tg CH₄ yr⁻¹), United Arab Emirates (1.57, 1.48–1.65 Tg CH₄ yr⁻¹), Oman (1.4, 1.1–1.8 Tg CH₄ yr⁻¹) and Turkmenistan (1.4, 1.0–1.7 Tg CH₄ yr⁻¹) (Figure 6b). For the top 10 emitting countries, emissions from the energy sector were the dominant source, except in Turkey. Among these countries where the energy sector was the primary contributor, fugitive emissions from oil and gas were the largest source, accounting for 70.7%–94.8% of total national emissions, with the exception of Kazakhstan. In Kazakhstan, fugitive emissions from oil and gas, and from coal contributed similarly to the national total, at 30.4%
<table><caption>**Table 3**
CH₄ Budget in Central and West Asia for the 2000s, 2010s, and the Year 2020</caption><thead><tr><th rowspan="2">Sectors</th><th rowspan="2"></th><th colspan="3">CH₄ (Tg CH₄ yr⁻¹)</th></tr><tr><th>2000–2009<br>Mean (min, max)</th><th>2010–2019<br>Mean (min, max)</th><th>2020<br>Mean (min, max)</th></tr></thead><tbody><tr><td rowspan="5">Anthropogenic Emissions</td><td>Energy</td><td>18.3 (17.2, 19.4)</td><td>22.4 (20.9, 24.0)</td><td>23.1 (21.7, 24.5)</td></tr><tr><td>Industry</td><td>4.3 × 10⁻² (4.1 × 10⁻², 4.4 × 10⁻²)</td><td>7.7 × 10⁻² (7.4 × 10⁻², 7.9 × 10⁻²)</td><td>9.0 × 10⁻² (8.4 × 10⁻², 9.6 × 10⁻²)</td></tr><tr><td>Agriculture</td><td>4.5 (4.3, 4.7)</td><td>5.5 (5.2, 5.8)</td><td>6.1 (5.7, 6.5)</td></tr><tr><td>Waste</td><td>4.6 (4.5, 4.7)</td><td>6.8 (6.7, 7.0)</td><td>7.9 (7.7, 8.0)</td></tr><tr><td>Subtotal</td><td>27.4 (26.2, 28.5)</td><td>34.8 (33.2, 36.4)</td><td>37.2 (35.7, 38.7)</td></tr><tr><td rowspan="5">Natural Sinks and Sources</td><td>Wetlands</td><td>8.1 × 10⁻¹ (3.5 × 10⁻¹, 1.3)</td><td>8.6 × 10⁻¹ (3.9 × 10⁻¹, 1.3)</td><td>8.3 × 10⁻¹ (4.1 × 10⁻¹, 1.3)</td></tr><tr><td>Wildfires</td><td>5.3 × 10⁻² (4.2 × 10⁻², 6.5 × 10⁻²)</td><td>4.0 × 10⁻² (3.1 × 10⁻², 4.9 × 10⁻²)</td><td>3.1 × 10⁻² (2.5 × 10⁻², 3.6 × 10⁻²)</td></tr><tr><td>Inland waters</td><td>4.9 (3.4, 6.7)</td><td>4.9 (3.4, 6.7)</td><td>4.9 (3.4, 6.7)</td></tr><tr><td>Geological seepages</td><td>6.0 (5.4, 6.6)</td><td>6.0 (5.4, 6.6)</td><td>6.0 (5.4, 6.6)</td></tr><tr><td>Subtotal</td><td>11.8 (10.1, 13.7)</td><td>11.8 (10.2, 13.7)</td><td>11.8 (10.1, 13.7)</td></tr><tr><td rowspan="2">Net Balance</td><td>BU approach</td><td>39.1 (37.1, 41.4)</td><td>46.6 (44.3, 49.1)</td><td>49.0 (46.8, 51.4)</td></tr><tr><td>TD approach</td><td>32.0 (29.0, 34.9)</td><td>38.9 (34.6, 43.2)</td><td>41.1 (36.8, 45.4)</td></tr></tbody></table>

and 31.6%, respectively. In contrast, in Turkey, the primary source of emissions was solid waste disposal, accounting for 44.2% of the country's total emissions.

### 3.2.1.2. Natural Sinks and Sources of CH₄

For the wetland CH₄ emissions in the CWA region, the ensemble annual mean was estimated to be a net source of 8.4 × 10⁻¹ (3.2 × 10⁻¹ to 1.4) Tg CH₄ yr⁻¹ during 2000–2020. Over this period, CH₄ emissions increased from 7.5 × 10⁻¹ (2.9 × 10⁻¹ to 1.2) Tg CH₄ yr⁻¹ in 2000 to 8.3 × 10⁻¹ (4.1 × 10⁻¹ to 1.3) Tg CH₄ yr⁻¹ in 2020, with an ensemble trend of 4.0 × 10⁻³ Tg CH₄ yr⁻² (*p* < 0.05) (Figure 7a). Although the interannual variability of the ensemble mean is low, with annual means ranging from between 7.3 × 10⁻¹ to 9.4 × 10⁻¹ Tg CH₄ yr⁻¹, there is considerable spread among the estimates from the 11 GMB models (Figure S1b in Supporting Information S1). The models show different trends in wetland CH₄ emissions. ORCHIDEE, JSBACH, ISAM and JULES show significant slight increasing trends, whereas the other models do not.

In the 2010s, the total regional CH₄ emission from wetlands in the CWA region was 8.6 × 10⁻¹ (3.9 × 10⁻¹ to 1.3) Tg CH₄ yr⁻¹ (Table 3), accounting for only 0.5% of the global wetland emissions during the same period (Saunois et al., 2024). Most of the wetland CH₄ emissions originated from Kazakhstan (2.8 × 10⁻¹, −8.5 × 10⁻² to 6.4 × 10⁻¹ Tg CH₄ yr⁻¹), Turkey (2.1 × 10⁻¹, 3.7 × 10⁻² to 3.8 × 10⁻¹ Tg CH₄ yr⁻¹), and Iran (1.6 × 10⁻¹, −4.5 × 10⁻² to 3.6 × 10⁻¹ Tg CH₄ yr⁻¹) (Figure 7b), primarily due to the large wetland areas present in these countries. According to WAD2Mv2 data, which is used to parameterize the maximum wetland extent in the GMB models, the wetlands in the CWA region are predominantly located in these areas, including central and northern Kazakhstan, central and eastern Turkey, and northern and southeastern Iran (Saunois et al., 2024; Zhang et al., 2021). Figures 7c and 7d displays the spatial distribution of ensemble wetland CH₄ emission rates and associated uncertainties derived from the 11 GMB models. The CWA region exhibited relatively low emission rates, with an average emission rate of 7.0 × 10⁻² g CH₄ m⁻² yr⁻¹. However, specific countries such as Cyprus, Georgia, Turkey, and Azerbaijan displayed higher rates, with mean emission rates of 1.4, 3.5 × 10⁻¹, 2.4 × 10⁻¹, and 1.6 × 10⁻¹ g CH₄ m⁻² yr⁻¹, respectively. Most areas within CWA displayed relatively low uncertainties in the CH₄ emission rates derived from the 11 models, particularly countries with low emission rates. Higher uncertainties were concentrated in the areas with higher CH₄ emission rates, specifically northeast Kazakhstan, southwest Iran, eastern Iraq, western Georgia, and the western edge side of Turkey.

For wildfire CH₄ emissions, the average in the CWA region from 2000 to 2020 was 4.6 × 10⁻² Tg CH₄ yr⁻¹, showing no significant upward or downward trend, with emissions fluctuating between 1.0 × 10⁻² and 1.1 × 10⁻¹ Tg CH₄ yr⁻¹ (Figure 7a). During the 2010s, wildfire CH₄ emissions averaged 4.0 × 10⁻² (3.1 × 10⁻² to 4.9 × 10⁻²) Tg CH₄ yr⁻¹ (Figure 7b), accounting for only 0.003% of the global total (1,442.8 Tg CH₄ yr⁻¹ (Van
<figure><img src="image_9.png" /></figure>

**Figure 7.** Temporal dynamics and spatial variations of natural CH₄ sources and sinks in the CWA region. (a) Interannual variability of regional CH₄ emissions from wetlands (green), inland waters (rivers: light blue; natural lakes: sky blue; reservoirs: medium blue; lakes regulated by dams: dark blue), wildfires (red), and geological sources (gray). (b) Decadal mean CH₄ fluxes at national and regional levels for the 2010s. (c) Spatial distribution of ensemble wetland CH₄ emission rates in the 2010s from the GMB models. (d) Spatial distribution of uncertainty (one standard deviation) in wetland CH₄ emission rate estimates from the GMB models during the 2010s.

Der Werf et al., 2017)), mainly because the region is characterized by arid and sparsely vegetated landscapes. The primary sources of these emissions were savanna, grassland, and shrubland fires, which contributed 95.0% (3.8 × 10⁻², 2.1 × 10⁻² to 5.5 × 10⁻² Tg CH₄ yr⁻¹) of the regional total, with most of these emissions occurring in Kazakhstan, which contributed 3.4 × 10⁻² (1.7 × 10⁻² to 5.1 × 10⁻²) Tg CH₄ yr⁻¹ (Figure 7b).

For inland water CH₄ emissions, the regional total was 4.9 (3.4–6.7) Tg CH₄ yr⁻¹, accounting for about 4.9% of the global inland water CH₄ emissions (100.3 Tg CH₄ yr⁻¹ (Lauerwald et al., 2023)) (Figure 4a, Table 3). Within the regional total, the largest portion came from natural lakes, totaling 4.0 (2.6–5.8) Tg CH₄ yr⁻¹, followed by emissions from reservoirs at 0.5 (0.3–0.6) Tg CH₄ yr⁻¹, lakes regulated by dams at 0.2 (0.1–0.3) Tg CH₄ yr⁻¹, and rivers at 0.2 Tg CH₄ yr⁻¹. Natural lake CH₄ emissions were primarily distributed across Kazakhstan, Uzbekistan, and Turkmenistan, with respective emissions of 1.6 (0.5–3.1), 0.8 (0.2–1.5), and 0.7 (0.2–1.3) Tg CH₄ yr⁻¹ (Figure 7b).

For geological CH₄ emissions in the CWA region, the total was 6.0 (5.4–6.6) Tg CH₄ yr⁻¹, accounting for 16.2% of the global total (37 Tg CH₄ yr⁻¹ (Etiope et al., 2019)) and making considerable contributions to the region's natural emissions. The primary sources were onshore seepage from mud volcanoes, and diffuse microseepage typically detected from oil-gas fields, each contributing 2.6 (1.5–3.7) Tg CH₄ yr⁻¹ and 2.6 (2.1–3.0) Tg CH₄ yr⁻¹ to the regional total. Large geological emissions were observed in Azerbaijan, with values of 2.9 (2.3–3.5) Tg CH₄ yr⁻¹.

### 3.2.2. CH₄ Budget Synthesis

For the CWA region, the total CH₄ budget estimated by the BU approaches was 37.0 (35.0–39.3) Tg CH₄ yr⁻¹ in 2000, increasing to 49.0 (46.8–51.4) Tg CH₄ yr⁻¹ in 2020, with a growth rate of 0.7 Tg CH₄ yr⁻² (Figure 8a). The TD approaches estimated 32.4 (28.2–36.5) Tg CH₄ yr⁻¹ in 2000, increasing to 41.1 (36.8–45.4) Tg CH₄ yr⁻¹ in 2020, with a growth rate of 0.6 Tg CH₄ yr⁻². The decadal means for the 2000s, 2010s, and the year 2020 by both approaches are shown in Table 3. Both approaches indicated that the CWA region was a growing net CH₄ source. Notably, the BU estimates averaged 7.4 Tg CH₄ yr⁻¹ higher than the TD estimates over the 2000–2020 period.
<figure><img src="image_10.png" /><figcaption>**Figure 8.** Temporal changes and decadal average of CH₄ budgets in the CWA region. (a) Comparison between the regional bottom-up (BU, orange) CH₄ total budget and the top-down (TD, blue) CH₄ budget during 2000–2020. Shaded area represents the uncertainty ranges. (b) BU anthropogenic (BU ant, gray), BU natural (BU nat, green), and BU total (BU tot, black dots) CH₄ budgets for each CWA country and the CWA region in the decade 2010–2019 (2010s). Error bars indicate the uncertainty ranges.</figcaption></figure>

According to the GMB project, the BU methods estimated higher global total CH₄ emissions than the TD approaches, with the discrepancy mainly attributed to uncertainties in natural emissions, particularly from wetlands and inland waters (Saunois et al., 2024). Additionally, given that fugitive emissions from the energy sector was the major CH₄ source in this region, the use of latest inventory data sets (EDGARv8, CEDS v2024_04_01) in the BU estimates, compared to older data sets (EDGARv6, GAINS) used by inversion systems, likely contributed to this discrepancy. Furthermore, for this study, we adopted the surface observation-based inversion estimates since they aligned with our research timeframe. However, as noted in the literature (Deng et al., 2022; Tibrewal et al., 2024), the limited number of surface observation stations in the CWA region (only one or two) introduces additional uncertainty into the TD estimates.

For the decade 2010–2019, the total CH₄ budget for the CWA region was estimated at 46.6 (44.3–49.1) Tg CH₄ yr⁻¹ by the BU approaches (Figure 8, Table 3). Anthropogenic and natural sources contributed 74.7% (34.8, 33.2–36.4 Tg CH₄ yr⁻¹) and 25.3% (11.8, 10.2–13.7 Tg CH₄ yr⁻¹) of the regional total, respectively. At the national level, Iran showed the highest budget (7.1, 6.2–8.1 Tg CH₄ yr⁻¹), followed by Iraq (4.8, 4.7–5.0 Tg CH₄ yr⁻¹), Kazakhstan (4.8, 3.5–6.3 Tg CH₄ yr⁻¹), Turkey (4.6, 4.2–5.0 Tg CH₄ yr⁻¹), and Saudi Arabia (4.0, 3.9–4.2 Tg CH₄ yr⁻¹), all of which showed similar magnitudes. Among the 24 countries in the region, Azerbaijan, Georgia, Armenia, and Kyrgyzstan were the only ones where natural sources dominated CH₄ emissions, contributing 87.0%, 76.2%, 68.8%, and 62.1% of their decadal means, respectively. In contrast, in all other countries, anthropogenic emissions were the primary source, particularly in economies heavily reliant on oil, such as such as the countries of the Organization of the Petroleum Exporting Countries (OPEC, 2024). For example, the United Arab Emirates (98.9%), Iran (85.5%), Iraq (84.2%), Kuwait (97.1%), and Saudi Arabia (96.3%) had significant contributions from anthropogenic sources.

## 3.3. N₂O Budget

### 3.3.1. Bottom-Up Approach

#### 3.3.1.1. Anthropogenic N₂O Emissions

The total anthropogenic N₂O emissions in the CWA showed a significant increase during the past two decades, from 3.6 × 10⁻¹ (3.3 × 10⁻¹ to 4.0 × 10⁻¹) Tg N₂O yr⁻¹ in 2000 to 5.5 × 10⁻¹ (5.0 × 10⁻¹ to 6.1 × 10⁻¹) Tg N₂O yr⁻¹ in 2020 (Figure 9a). A closer examination of sector-specific trends reveals that emissions from the energy sector increased from 2.9 × 10⁻² (2.6 × 10⁻² to 3.1 × 10⁻²) Tg N₂O yr⁻¹ in 2000 to 4.5 × 10⁻² (4.2 × 10⁻² to 4.9 × 10⁻²) Tg N₂O yr⁻¹ in 2020, growing at a rate of 1.2 × 10⁻³ Tg N₂O yr⁻² (*p* < 0.05, Figure 9a). The rise of N₂O emission in the energy sector was mainly due to the increase of fossil fuels combustion in road transportation (Table S3 in Supporting Information S2). The N₂O emissions in the industrial sector exhibited a three-phase pattern of change over the period from 2000 to 2020 (Figure 9a). Initially, there was an increase from
<figure><img src="image_11.png" /><figcaption>**Figure 9.** Annual anthropogenic N₂O emissions by sectors for (a) CWA region during 2000–2020 and for (b) CWA region and countries in the 2010s. The yellow, blue, green, and brown colors represent emissions from the energy, industry, agriculture and waste sectors, respectively.</figcaption></figure>

4.6 × 10⁻² (4.0 × 10⁻² to 5.1 × 10⁻²) Tg N₂O yr⁻¹ in 2000 to 8.9 × 10⁻² (8.5 × 10⁻² to 9.4 × 10⁻²) Tg N₂O yr⁻¹ in 2007, corresponding to an annual growth rate of 6.7 × 10⁻³ Tg N₂O yr⁻². This increasing trend was followed by a marked decline, culminating in an emission level of 5.5 × 10⁻² (5.1 × 10⁻² to 6.0 × 10⁻²) Tg N₂O yr⁻¹ in 2011, caused mainly by the Iranian chemical industry. Then a fast increase was observed in 2012, with emissions increasing to 8.1 × 10⁻² (4.8 × 10⁻² to 1.1 × 10⁻¹) Tg N₂O yr⁻¹. Thereafter, emissions from the industrial sector continued to increase at a moderate rate of 1.6 × 10⁻³ Tg N₂O yr⁻² during 2013–2020. The observed changes in N₂O emissions were predominantly attributed to activities within the chemical industry. The agricultural sector accounted for the largest proportion, 69.6%, of total anthropogenic N₂O emissions in the CWA over the last 21 years. N₂O emissions consistently increased from 2.7 × 10⁻¹ (2.3 × 10⁻¹ to 3.0 × 10⁻¹) Tg N₂O yr⁻¹ in 2000 to 3.8 × 10⁻¹ (3.4 × 10⁻¹ to 4.2 × 10⁻¹) Tg N₂O yr⁻¹ in 2020, at an average annual increase rate of 5.2 × 10⁻³ Tg N₂O yr⁻² (*p* < 0.05, Figure 9a). The primary source within this sector was direct N₂O emissions from managed soils. Emissions from the waste sector, while minor, also experienced a slight growth over the past two decades, rising from 2.0 × 10⁻² (1.9 × 10⁻² to 2.0 × 10⁻²) Tg N₂O yr⁻¹ in 2000 to 3.05 × 10⁻² (2.99 × 10⁻² to 3.15 × 10⁻²) Tg N₂O yr⁻¹ in 2020, with wastewater treatment and discharge being the predominant contributing factor.

In the 2010s, anthropogenic N₂O emissions in the CWA region were estimated at 4.9 × 10⁻¹ (4.4 × 10⁻¹ to 5.4 × 10⁻¹) Tg N₂O yr⁻¹ (Table 4), accounting for 4.8% of the global total anthropogenic N₂O emissions (10.2 Tg

<table><caption>**Table 4**
N₂O Budget in Central and West Asia for the 2000s, 2010s, and the Year 2020</caption><thead><tr><th rowspan="3">Sectors</th><th rowspan="3"></th><th colspan="3">N₂O (Tg N₂O yr⁻¹)</th></tr><tr><th>2000–2009</th><th>2010–2019</th><th>2020</th></tr><tr><th>Mean (min, max)</th><th>Mean (min, max)</th><th>Mean (min, max)</th></tr></thead><tbody><tr><td rowspan="5">Anthropogenic Emissions</td><td>Energy</td><td>3.4 × 10⁻² (3.1 × 10⁻², 3.7 × 10⁻²)</td><td>4.8 × 10⁻² (4.5 × 10⁻², 5.1 × 10⁻²)</td><td>4.5 × 10⁻² (4.2 × 10⁻², 4.9 × 10⁻²)</td></tr><tr><td>Industry</td><td>6.2 × 10⁻² (5.7 × 10⁻², 6.7 × 10⁻²)</td><td>7.8 × 10⁻² (4.8 × 10⁻², 1.1 × 10⁻¹)</td><td>9.6 × 10⁻² (5.3 × 10⁻², 1.4 × 10⁻¹)</td></tr><tr><td>Agriculture</td><td>2.9 × 10⁻¹ (2.6 × 10⁻¹, 3.2 × 10⁻¹)</td><td>3.4 × 10⁻¹ (3.0 × 10⁻¹, 3.7 × 10⁻¹)</td><td>3.8 × 10⁻¹ (3.4 × 10⁻¹, 4.2 × 10⁻¹)</td></tr><tr><td>Waste</td><td>2.2 × 10⁻² (2.1 × 10⁻², 2.3 × 10⁻²)</td><td>2.8 × 10⁻² (2.7 × 10⁻², 2.9 × 10⁻²)</td><td>3.1 × 10⁻² (3.0 × 10⁻², 3.1 × 10⁻²)</td></tr><tr><td>Subtotal</td><td>4.1 × 10⁻¹ (3.7 × 10⁻¹, 4.4 × 10⁻¹)</td><td>4.9 × 10⁻¹ (4.4 × 10⁻¹, 5.4 × 10⁻¹)</td><td>5.5 × 10⁻¹ (5.0 × 10⁻¹, 6.1 × 10⁻¹)</td></tr><tr><td rowspan="4">Natural Sinks and Sources</td><td>Natural soils</td><td>2.0 × 10⁻¹ (1.5 × 10⁻¹, 2.5 × 10⁻¹)</td><td>2.1 × 10⁻¹ (1.5 × 10⁻¹, 2.7 × 10⁻¹)</td><td>2.0 × 10⁻¹ (1.5 × 10⁻¹, 2.5 × 10⁻¹)</td></tr><tr><td>Wildfires</td><td>5.4 × 10⁻³ (4.0 × 10⁻³, 6.8 × 10⁻³)</td><td>4.0 × 10⁻³ (3.0 × 10⁻³, 5.0 × 10⁻³)</td><td>3.1 × 10⁻³ (2.4 × 10⁻³, 3.7 × 10⁻³)</td></tr><tr><td>Inland waters</td><td>1.4 × 10⁻² (1.1 × 10⁻², 1.7 × 10⁻²)</td><td>1.4 × 10⁻² (1.1 × 10⁻², 1.7 × 10⁻²)</td><td>1.4 × 10⁻² (1.1 × 10⁻², 1.7 × 10⁻²)</td></tr><tr><td>Subtotal</td><td>2.2 × 10⁻¹ (1.7 × 10⁻¹, 2.7 × 10⁻¹)</td><td>2.3 × 10⁻¹ (1.7 × 10⁻¹, 2.9 × 10⁻¹)</td><td>2.2 × 10⁻¹ (1.6 × 10⁻¹, 2.7 × 10⁻¹)</td></tr><tr><td rowspan="2">Net Balance</td><td>BU approach</td><td>6.3 × 10⁻¹ (5.7 × 10⁻¹, 6.9 × 10⁻¹)</td><td>7.2 × 10⁻¹ (6.5 × 10⁻¹, 8.0 × 10⁻¹)</td><td>7.7 × 10⁻¹ (6.9 × 10⁻¹, 8.5 × 10⁻¹)</td></tr><tr><td>TD approach</td><td>8.5 × 10⁻¹ (6.9 × 10⁻¹, 1.0)</td><td>9.8 × 10⁻¹ (7.5 × 10⁻¹, 1.2)</td><td>1.1 (1.1, 1.2)</td></tr></tbody></table>
<figure><img src="image_12.png" /><figcaption>**Figure 10.** Temporal dynamics and spatial variations of natural N₂O fluxes in the CWA Region. (a) Interannual variability of N₂O emissions from natural soils (green), rivers (light blue), natural lakes (sky blue), reservoirs (medium blue), lakes regulated by dams (dark blue), and wildfires (red). (b) Decadal mean N₂O fluxes at national and regional levels for the 2010s. (c) Spatial distribution of ensemble natural soil N₂O emission rates in the 2010s derived from NMIP2 models. (d) Spatial distribution of uncertainty (one standard deviation) in NMIP2 models' estimates of natural soil N₂O emission rates in the 2010s.</figcaption></figure>

N₂O yr⁻¹ (Tian et al., 2024)). The top 10 emitters were ranked as follows: Turkey (1.2 × 10⁻¹, 9.0 × 10⁻² to 1.6 × 10⁻¹ Tg N₂O yr⁻¹), Iran (9.7 × 10⁻², 7.7 × 10⁻² to 1.2 × 10⁻¹ Tg N₂O yr⁻¹), Uzbekistan (4.6 × 10⁻², 3.6 × 10⁻² to 5.6 × 10⁻² Tg N₂O yr⁻¹), Kazakhstan (3.7 × 10⁻², 2.0 × 10⁻² to 5.3 × 10⁻² Tg N₂O yr⁻¹), Saudi Arabia (2.6 × 10⁻², 2.1 × 10⁻² to 3.1 × 10⁻² Tg N₂O yr⁻¹), Iraq (2.2 × 10⁻², 1.6 × 10⁻² to 2.8 × 10⁻² Tg N₂O yr⁻¹), Turkmenistan (2.1 × 10⁻², 1.7 × 10⁻² to 2.5 × 10⁻² Tg N₂O yr⁻¹), Afghanistan (1.8 × 10⁻², 9.6 × 10⁻³ to 2.6 × 10⁻² Tg N₂O yr⁻¹), Yemen (1.8 × 10⁻², 9.9 × 10⁻³ to 2.5 × 10⁻² Tg N₂O yr⁻¹), and Syria (1.7 × 10⁻², 1.1 × 10⁻² to 2.2 × 10⁻² Tg N₂O yr⁻¹) (Figure 9b). Combined, these countries accounted for 86.7% of the region's anthropogenic N₂O emissions. Direct N₂O emissions from N additions in agricultural soils were the primary source in these top 10 countries, contributing between 25.9% and 52.9% of their decadal averages in the 2010s.

### 3.3.1.2. Natural Sinks and Sources of N₂O Emissions

The natural soil N₂O emissions remained relatively stable during 2000–2020. The ensemble mean of N₂O emissions, derived from the eight NMIP2 models, was 2.1 × 10⁻¹ (1.0 × 10⁻¹ to 3.1 × 10⁻¹) Tg N₂O yr⁻¹, fluctuating between 1.9 × 10⁻¹ and 2.3 × 10⁻¹ Tg N₂O yr⁻¹ (Figure 10a). Furthermore, significant divergence was observed among the estimates from the eight models. Notably, ORCHIDEE exhibited a significant increase (*p* < 0.05) at a rate of 3.0 × 10⁻³ Tg N₂O yr⁻¹, a trend not mirrored by the other models. OCN presented the highest estimate, averaging 3.9 × 10⁻¹ Tg N₂O yr⁻¹, markedly surpassing that of the DLEM model, which averaged 5.3 × 10⁻² Tg N₂O yr⁻¹ (Figure S1c in Supporting Information S1).

In the 2010s, the total regional N₂O emission from natural soils in the CWA region was 2.1 × 10⁻¹ (1.5 × 10⁻¹ to 2.7 × 10⁻¹) Tg N₂O yr⁻¹ (Figure 10b and Table 4), accounting for 2.1% of the global natural soil emissions during the same period (10.1 Tg N₂O yr⁻¹ (Tian et al., 2024)). Most of the natural soil N₂O emissions originated from Kazakhstan (5.6 × 10⁻², 1.9 × 10⁻² to 9.4 × 10⁻² Tg N₂O yr⁻¹) and Turkey (4.6 × 10⁻², 1.6 × 10⁻² to 7.5 × 10⁻² Tg N₂O yr⁻¹). Figures 10c and 10d present the spatial distribution of ensemble natural N₂O emission rates and associated uncertainties from the eight NMIP2 models for the most recent decade, the 2010s. The regional average N₂O emission rate from natural soils was 1.7 × 10⁻² g N₂O m⁻² yr⁻¹ during the 2010s.
<figure><img src="image_13.png" /><figcaption>**Figure 11.** Temporal changes and decadal average of N₂O budgets in the CWA region. (a) Comparison between the regional bottom-up (BU, orange) N₂O total budget and the top-down (TD, blue) N₂O budget during 2000–2020. Shaded area represents the uncertainty ranges. (b) BU anthropogenic (BU ant, gray), BU natural (BU nat, green), and BU total (BU tot, black dots) N₂O budgets for each CWA country and the CWA region in the decade 2010–2019 (2010s). Error bars indicate the uncertainty ranges.</figcaption></figure>

Countries such as Jordan, Turkmenistan, Bahrain, Kuwait, Oman, Saudi Arabia, the United Arab Emirates, and Qatar exhibited emissions lower than 1.0 × 10⁻² g N₂O m⁻² yr⁻¹. Emission hotspots of soil N₂O in the CWA region were Georgia (6.1 × 10⁻² g N₂O m⁻² yr⁻¹), Turkey (5.4 × 10⁻² g N₂O m⁻² yr⁻¹), Armenia (4.8 × 10⁻² g N₂O m⁻² yr⁻¹), Lebanon (4.6 × 10⁻² g N₂O m⁻² yr⁻¹), Azerbaijan (4.3 × 10⁻² g N₂O m⁻² yr⁻¹), Tajikistan (2.9 × 10⁻² g N₂O m⁻² yr⁻¹), Cyprus (2.7 × 10⁻² g N₂O m⁻² yr⁻¹), Kyrgyzstan (2.4 × 10⁻² g N₂O m⁻² yr⁻¹), and Afghanistan (2.4 × 10⁻² g N₂O m⁻² yr⁻¹). Areas with higher N₂O emission rates, such as Turkey, Georgia, western Kyrgyzstan, western Tajikistan, and central and northeastern Afghanistan, correspondingly showed higher uncertainties. Conversely, areas with lower emission rates displayed relatively low uncertainties in their N₂O emission rates.

The wildfire N₂O emissions accounted for a small proportion of the N₂O emissions from natural sources (Figure 10a). Regional wildfire N₂O emissions fluctuated between 1.0 × 10⁻³ (8.5 × 10⁻⁴ to 1.2 × 10⁻³) and 1.1 × 10⁻² (8.3 × 10⁻³ to 1.4 × 10⁻²) Tg N₂O yr⁻¹, with no significant decreasing or increasing trend observed. During the decade 2010–2019, the mean value of regional N₂O emissions from wildfires was 4.0 × 10⁻³ (3.0 × 10⁻³–5.0 × 10⁻³) Tg N₂O yr⁻¹, primarily from Kazakhstan, with a decadal mean of 3.6 × 10⁻³ (2.5 × 10⁻³–4.6 × 10⁻³) Tg N₂O yr⁻¹ (Figure 10b).

Natural N₂O emissions from inland waters were much smaller than emissions from the soils (Figure 10a). These emissions had an average value of 1.4 × 10⁻² Tg N₂O yr⁻¹, ranging from 1.1 × 10⁻² to 1.7 × 10⁻² Tg N₂O yr⁻¹, accounting for 4.4% of the global inland water N₂O emissions (3.2 × 10⁻¹ Tg N₂O yr⁻¹, (Lauerwald et al., 2023)). Natural lakes were the largest source, emitting 8.2 × 10⁻³ (5.6 × 10⁻³ to 1.1 × 10⁻²) Tg N₂O yr⁻¹ and accounting for 57.3% of the regional total natural emissions from inland waters.

### 3.3.2. N₂O Budget Synthesis

For the CWA region, the BU approaches suggested that the total N₂O budget increased from 5.7 × 10⁻¹ (5.0 × 10⁻¹ to 6.4 × 10⁻¹) Tg N₂O yr⁻¹ in 2000 to 7.7 × 10⁻¹ (6.9 × 10⁻¹ to 8.5 × 10⁻¹) Tg N₂O yr⁻¹ in 2020, at a mean rate of 9.5 × 10⁻³ Tg N₂O yr⁻² (Figure 11a). The TD approaches indicated that the regional total N₂O budget increased from 8.4 × 10⁻¹ (6.4 × 10⁻¹ to 1.0) Tg N₂O yr⁻¹ in 2000 to 1.14 (1.06–1.22) Tg N₂O yr⁻¹ in 2020, at a mean rate of 1.3 × 10⁻² Tg N₂O yr⁻². Both methods suggest that the CWA region has been a small but increasing N₂O source during 2000–2020. However, despite the agreement in trends, the BU estimates were generally about 2.5 × 10⁻¹ Tg N₂O yr⁻¹ lower than the TD estimates. The reason for this discrepancy is not yet fully understood and requires further investigation, but one potential cause could be the limited number and sparse distribution of N₂O measurement stations, which prevents the inversions from sufficiently adjusting the prior estimates. Specifically, GEOSChem, INVICAT, and PyVAR-CAMS used the TBM O-CN model (Zaehle et al., 2011) to build prior flux distributions for natural soil emissions, while MIROC4-ACTM used the VISIT model (Ito et al., 2018). Both TBMs tended to estimate higher emissions compared to the ensemble mean of the
<figure><img src="image_14.png" /><figcaption>**Figure 12.** Regional and national CO₂ (blue), CH₄ (green), N₂O (purple), and GHG (black) budgets during 2000–2020. Solid lines represent estimates by BU approached and dashed lines represent estimates by TD approaches.</figcaption></figure>

eight TBMs used in the BU estimates (Figure S1c in Supporting Information S1), potentially leading to the higher prior flux estimates. Furthermore, the lack of measurement stations in the CWA region limits the ability of atmospheric inversions to adjust their prior based on in-situ observation data, resulting in higher posterior estimates from TD compared to BU estimates. Therefore, it is crucial to increase the number of observation sites and conduct regular aircraft profiles to improve the accuracy of inversion models and reduce posterior uncertainty (Tian et al., 2024).

For the decade 2010–2019, the total N₂O budget for the CWA region was estimated at 7.2 × 10⁻¹ (6.4 × 10⁻¹ to 8.0 × 10⁻¹) Tg N₂O yr⁻¹ by the BU approaches (Figure 10b, Table 4). Anthropogenic sources contributed 68.0% (4.9 × 10⁻¹, 4.4 × 10⁻¹ to 5.4 × 10⁻¹ Tg N₂O yr⁻¹) of the regional total, while natural sources accounted for 32.0% (2.3 × 10⁻¹, 1.7 × 10⁻¹ to 2.9 × 10⁻¹ Tg N₂O yr⁻¹). At the national level, Turkey had the largest N₂O budget, with emissions of 1.7 × 10⁻¹ (1.3 × 10⁻¹ to 2.2 × 10⁻¹) Tg N₂O yr⁻¹. Among the 24 countries in the region, Kazakhstan, Georgia, Kyrgyzstan, and Afghanistan, were the ones where natural sources dominated N₂O emissions, contributing 64.1%, 53.9%, 51.9%, and 51.3% of their decadal means, respectively. In all other countries, anthropogenic emissions were the primary source, accounting for 52.0%–97.4% of the total emissions. Direct N₂O emissions from agricultural soil, driven by N input, were the largest source of N₂O emissions.

### 3.4. GHG Budget Synthesis

Employing the GWP over the 100-year timeframe from the IPCC Sixth Assessment Report (Forster et al., 2021), we calculated a composite budget for the three GHGs, expressed in CO₂ equivalents (CO₂eq), for the period from 2000 to 2020, covering both the CWA region and each country within it (Figure 12). This approach enabled us to
<figure><img src="image_15.png" /><figcaption>**Figure 13.** Central and West Asia GHG budget in the decade 2010-2019. The color arrows and numbers represent GHG fluxes in Tg CO₂eq yr⁻¹ as follows: CO₂ (sky blue), CH₄ (olive green); N₂O (purple). Definitions and explanations of the flux terms can be found in the Methods section.</figcaption></figure>

assess the overall GHG budgets at both the regional and national levels and facilitated a comprehensive comparison of the relative climate impacts of each gas, highlighting the primary sources of emissions and their contributions to global warming (Figure 13).

### 3.4.1. Regional Budgets

The net GHG budget of CWA, as estimated by the BU and TD approaches, showed comparable magnitudes and trends from 2000 to 2020 (Figure 12a). According to the BU estimates, the net GHG budget increased from 2,921.0 (2,729.0–3,116.6) Tg CO₂eq yr⁻¹ in 2000 to 4,396.5 (4,233.7–4,563.6) Tg CO₂eq yr⁻¹ in 2020, with an average increase rate of 94.8 Tg CO₂eq yr⁻² (*p* < 0.05). In contrast, TD approaches suggested the regional budget rose from 2,416.6 (1,454.3–3,379.0) Tg CO₂eq yr⁻¹ in 2000 to 4,698.3 (4,224.6–5,172.1) Tg CO₂eq yr⁻¹ in 2020, implying a higher increased rate of 110.2 Tg CO₂eq yr⁻² (*p* < 0.05). Both approaches converge on the conclusion that the CWA region acted as a net GHG source throughout the last two decades. According to BU estimates, the increase was mainly driven by anthropogenic CO₂ emissions, which rose from 1,615.9 (1,590.0–1,641.7) Tg CO₂eq yr⁻¹ in 2000 to 2,958.1 (2,922.1–2,994.2) Tg CO₂eq yr⁻¹ in 2020, with an average increase rate of 76.9 Tg CO₂eq yr⁻¹ yr⁻¹ (*p* < 0.05). Meanwhile, the C uptake of natural ecosystems showed no significant increase over the last two decades.

In addition, the utilization of the common unit GWP100 underscores the significant contribution of non-CO₂ gasses to the region's C sources, particularly CH₄. CH₄ emissions, as estimated by the BU approaches, accounted for 1,337.1 (1,271.7–1,408.5) Tg CO₂eq yr⁻¹ during 2010–2019, representing 32.0% of the BU total GHG budget. The TD approaches estimated these emissions at 1,051.5 (935.5–1,167.6)) Tg CO₂eq yr⁻¹, constituting 24.5% of the TD total GHG budget (Table 5, Figure S4 in Supporting Information S1). Given CH₄'s radiative forcing and atmospheric lifetime characteristics, as well as its significance as a key emission source in the CWA region, we recalculated the annual GHG budgets using the GWP20 metric. Under this accounting, the decadal CH₄ budget represents 57.2% and 48.9% of the BU and TD total GHG budgets, respectively, underscoring its even larger impact on shorter timescales.

For the decade 2010–2019, the net GHG budget from BU estimates shows net emissions of 4,175.2 (4,054.8–4,301.3) Tg CO₂eq yr⁻¹, with CO₂ contributing 2,641.3 (2,542.2–2,743.2) Tg CO₂eq yr⁻¹, followed by CH₄ at 1,337.1 (1,271.7–1,408.5) Tg CO₂eq yr⁻¹ and N₂O at 196.8 (176.3–217.3) Tg CO₂eq yr⁻¹ (Table 5 and Figure 13). The estimates from the TD approach suggest slightly higher net emissions of 4,292.9 (3,760.0–4,175.2).
<table><caption>**Table 5**
Regional GHG Budget (2010–2019) Based on GWP100 and GWP20 Metrics</caption><thead><tr><th>Sectors</th><th></th><th>CO<sub>2</sub> mean (min, max) (GWP100)</th><th>CH<sub>4</sub> mean (min, max) (GWP100)</th><th>N<sub>2</sub>O mean (min, max) (GWP100)</th><th>Net GHG mean (min, max) (GWP100)</th><th>CO<sub>2</sub> mean (min, max) (GWP20)</th><th>CH<sub>4</sub> mean (min, max) (GWP20)</th><th>N<sub>2</sub>O mean (min, max) (GWP20)</th><th>Net GHG mean (min, max) (GWP20)</th></tr></thead><tbody><tr><td rowspan="5">Anthropogenic Emissions</td><td>Energy</td><td>2104.6 (2078.0, 2131.1)</td><td>668.1 (621.5, 714.8)</td><td>13.1 (12.2, 14.0)</td><td>2785.8 (2732.2, 2839.4)</td><td>2104.6 (2078.0, 2131.1)</td><td>1849.7 (1720.7, 1978.8)</td><td>13.1 (12.2, 14.0)</td><td>3967.4 (3835.6, 4099.1)</td></tr><tr><td>Industry</td><td>703.8 (691.1, 716.6)</td><td>2.1 (2.1, 2.2)</td><td>21.3 (13.2, 29.4)</td><td>727.3 (712.2, 742.4)</td><td>703.8 (691.1, 716.6)</td><td>6.2 (6.0, 6.4)</td><td>21.3 (13.2, 29.4)</td><td>731.3 (716.2, 746.5)</td></tr><tr><td>Agriculture</td><td>8.0 (7.7, 8.3)</td><td>148.2 (139.6, 156.7)</td><td>91.8 (81.7, 102.0)</td><td>248.0 (234.7, 261.3)</td><td>8.0 (7.7, 8.3)</td><td>437.4 (412.3, 462.6)</td><td>91.8 (81.7, 102.0)</td><td>537.2 (510.1, 564.4)</td></tr><tr><td>Waste</td><td>1.5 (1.2, 2.0)</td><td>184.5 (181.3, 187.8)</td><td>7.6 (7.5, 7.9)</td><td>193.7 (190.4, 197.0)</td><td>1.5 (1.2, 2.0)</td><td>544.7 (535.2, 554.3)</td><td>7.6 (7.5, 7.9)</td><td>553.9 (544.3, 563.5)</td></tr><tr><td>Subtotal</td><td>2817.9 (2788.5, 2847.3)</td><td>1003.0 (955.5, 1050.5)</td><td>133.9 (120.9, 146.9)</td><td>3954.8 (3897.4, 4012.1)</td><td>2817.9 (2788.5, 2847.3)</td><td>2838.0 (2706.2, 2969.9)</td><td>133.9 (120.9, 146.9)</td><td>5789.8 (5654.1, 5925.5)</td></tr><tr><td rowspan="7">Natural sources and sinks</td><td>NBP</td><td>-249.6 (-343.5, -155.7)</td><td>-</td><td>-</td><td>-249.6 (-343.5, -155.7)</td><td>-249.6 (-343.5, -155.7)</td><td>-</td><td>-</td><td>-249.6 (-343.5, -155.7)</td></tr><tr><td>Wetlands</td><td>-</td><td>23.2 (10.5, 35.9)</td><td>-</td><td>23.2 (10.5, 35.9)</td><td>-</td><td>68.5 (31.0, 105.9)</td><td>-</td><td>68.5 (31.0, 105.9)</td></tr><tr><td>Natural soils</td><td>-</td><td>-</td><td>58.0 (42.1, 73.8)</td><td>58.0 (42.1, 73.8)</td><td>-</td><td>-</td><td>58.0 (42.1, 73.8)</td><td>58.0 (42.1, 73.8)</td></tr><tr><td>Inland waters</td><td>73.0 (61.7, 99.4)</td><td>132.3 (93.1, 180.8)</td><td>3.9 (3.1, 4.7)</td><td>209.2 (168.4, 264.4)</td><td>73.0 (61.7, 99.4)</td><td>390.5 (274.7, 533.7)</td><td>3.9 (3.1, 4.7)</td><td>467.4 (351.1, 613.0)</td></tr><tr><td>Wildfires</td><td>-</td><td>1.1 (0.8, 1.3)</td><td>1.1 (0.8, 1.4)</td><td>2.2 (1.8, 2.5)</td><td>-</td><td>3.2 (2.5, 3.9)</td><td>1.1 (0.8, 1.4)</td><td>4.3 (3.5, 5.0)</td></tr><tr><td>Geological seepages</td><td>-</td><td>177.6 (159.5, 195.7)</td><td>-</td><td>177.6 (159.5, 195.7)</td><td>-</td><td>493.6 (443.5, 543.8)</td><td>-</td><td>493.6 (443.5, 543.8)</td></tr><tr><td>Subtotal</td><td>-176.6 (-271.2, -79.1)</td><td>334.1 (289.1, 387.4)</td><td>63.0 (47.1, 78.8)</td><td>220.5 (114.5, 332.8)</td><td>-176.6 (-271.2, -79.1)</td><td>955.8 (824.2, 1112.0)</td><td>63.0 (47.1, 78.8)</td><td>842.1 (679.3, 1027.0)</td></tr><tr><td rowspan="2">Net Balance</td><td>BU budget</td><td>2641.3 (2542.2, 2743.2)</td><td>1337.1 (1271.7, 1408.5)</td><td>196.8 (176.3, 217.3)</td><td>4175.2 (4054.8, 4301.3)</td><td>2641.3 (2542.2, 2743.2)</td><td>3793.9 (3607.5, 3998.3)</td><td>196.8 (176.3, 217.3)</td><td>6632.0 (6420.0, 6861.3)</td></tr><tr><td>TD budget</td><td>2974.4 (2457.9, 3490.8)</td><td>1051.5 (935.5, 1167.6)</td><td>267.0 (205.4, 328.6)</td><td>4292.9 (3760.0, 4825.8)</td><td>2974.4 (2457.9, 3490.8)</td><td>3104.0 (2761.5, 3446.6)</td><td>267.0 (205.4, 328.6)</td><td>6345.4 (5722.6, 6968.2)</td></tr></tbody></table>

4,825.8) Tg CO₂eq yr⁻¹, with CO₂ contributing 2,974.4 (2,457.9–3,490.8) Tg CO₂eq yr⁻¹, followed by CH₄ at 1,051.5 (935.5–1,167.6) Tg CO₂eq yr⁻¹ and N₂O at 267.0 (205.4–328.6) Tg CO₂eq yr⁻¹ (Table 5 and Figure 13). According to the BU estimates, the energy sector was responsible for the highest emissions, contributing 66.7% of the total GHG budget, with 2,785.8 (2,732.2–2,839.4) Tg CO₂eq yr⁻¹. Terrestrial natural ecosystems were a net C source, with a decadal mean emission of 220.5 (114.5–332.8) Tg CO₂eq yr⁻¹. The net C uptake capacity could only offset 44.5% of the regional natural emissions of CH₄ and N₂O.
### 3.4.2. National GHG Budgets

To assess variations in the GHG budgets across the CWA region, we conducted a further analysis of the temporal trends and magnitudes of the three GHGs for 24 countries over the period 2000–2020 using GWP100 metrics (Figures 12b–12y).

Among all countries, Saudi Arabia, Iran, Turkey, Iraq, the United Arab Emirates, Qatar, Oman, Kuwait, Turkmenistan, Azerbaijan, Bahrain, Lebanon, Jordan, Israel, and Tajikistan exhibited significant increasing trends in their GHG budgets ($p < 0.05$), with respective increasing rates of 21.3, 16.7, 13.0, 10.4, 7.0, 6.6, 3.6, 2.7, 1.9, 1.5, 1.3, 0.8, 0.6, 0.5, and 0.5 Tg CO₂eq yr⁻². The primary drivers of the increases in the net GHG budgets of Saudi Arabia, Iran, Turkey, Kuwait, Turkmenistan, Bahrain, and Lebanon were anthropogenic CO₂ emissions from the combustion of fossil fuels to generate electricity and heat. In Iraq, the main contributor was fugitive CH₄ emissions from the oil and natural gas industry, while in the United Arab Emirates and Israel, the increase in GHG budgets was caused by CO₂ emissions from fuel combustion in manufacturing industries and construction. For Qatar, the increase was driven by CO₂ emissions from fossil fuel combustion activities supporting the refining of petroleum products. In Oman, the GHG budget was mainly driven by an increase in CO₂ emissions from the residential sector, whereas in Azerbaijan and Jordan, CO₂ emissions from road transportation were the primary source of GHG budget. Finally, in Tajikistan, the increase in the GHG budget was driven by CH₄ emissions from livestock enteric fermentation.

In contrast, Syria and Yemen exhibited a significant but slow declining trend in their GHG budgets between 2000 and 2020, with reduction rates of -3.4 and -1.3 Tg CO₂eq yr⁻², respectively. The decrease in Syria's GHG budget was primarily driven by a reduction in CO₂ emissions from electricity generation, combined heat and power generation, and heat plants. In Yemen, the decline was mainly due to a reduction in fugitive CH₄ emissions from oil and natural gas systems. Aside from the aforementioned countries, no significant increasing or decreasing trends in GHG budgets were observed in Afghanistan, Armenia, Cyprus, Georgia, Kazakhstan, Kyrgyzstan, and Uzbekistan.

## 4. Discussion

Our study provides the first GHG budgets of the three most important GHGs (CO₂, CH₄, and N₂O) and their synthesis for the CWA region and its countries over the period 2000–2020, using both BU and TD approaches. Both approaches indicate that the CWA region was a net source of GHGs during the last two decades, with average annual net GHG emissions in GWP100 metrics of 3,711.6 (3,620.2–3,809.9) Tg CO₂eq yr⁻¹ (BU) and 3,710.5 (3,100.5–4,320.5) Tg CO₂eq yr⁻¹ (TD). Furthermore, the CWA region showed continuous growth in the net budgets, with average rates ranging from 94.8 (BU) to 110.2 Tg CO₂eq yr⁻² (TD) over the same period. The BU approach reveals that these increases were predominantly driven by human activities and were unevenly contributed across sectors or countries. These findings highlight the urgent need for targeted mitigation strategies to comprehensively address the emissions of CO₂, CH₄, and N₂O. In this section, we further analyze the GHG budgets along with the sources of uncertainties associated with the BU and TD estimates. First, we will compare our regional budget results with the global budgets and trends, which is an aspect previously unexplored. We then examine the main sectors or human activities driving the trends in both anthropogenic and natural emissions, compare these findings with existing research, and evaluate the potential for mitigation. Lastly, we assess the BU and TD estimates to identify uncertainties associated with each method, highlight missing elements in budget development, and suggest ways for future improvements.

### 4.1. Insights From the GHG Budgets

#### 4.1.1. CO₂ Budget

CO₂ was the main component of the CWA GHG budget contributing 61.6% of the BU total GHG budget and 67.2% of the TD total GHG budget in GWP 100 during 2000–2020 and showed highest increase rate among the three GHGs (Figure 12a). The CWA region contributed 9.6% of the global CO₂ budget in the 2010s, based on the BU-estimated global total of 27,561.5 (23,945.9–31,177.1) Tg CO₂ (Friedlingstein et al., 2023) (Table 6). According to the inventories, the growth was predominantly driven by increases in CO₂ emissions from the energy sector's fossil fuel combustion for electricity and heat production, road transportation, and industrial fossil fuel combustion, which contributed 44.0%, 22.3%, and 17.4% to the regional total anthropogenic CO₂ increase
<table><caption>**Table 6**
Comparison of the Regional GHG Budgets From This Study and the Global Estimates From Global Projects for the 2010s</caption><thead><tr><th>Gases</th><th></th><th>Regional mean (min, max)</th><th>Global mean (min, max)</th><th>Proportion of the global total (%)</th><th>Reference</th></tr></thead><tbody><tr><td rowspan="2">CO<sub>2</sub> (Tg CO<sub>2</sub> yr<sup>-1</sup>)</td><td>BU budget</td><td>2641.3 (2542.2, 2743.2)</td><td>27561.5 (23945.9, 31177.1)</td><td>9.6%</td><td>Friedlingstein et al. (2023)</td></tr><tr><td>TD budget</td><td>2974.4 (2457.9, 3490.8)</td><td>-</td><td>-</td><td></td></tr><tr><td rowspan="2">CH<sub>4</sub> (Tg CH<sub>4</sub> yr<sup>-1</sup>)</td><td>BU budget</td><td>46.6 (44.3, 49.1)</td><td>669 (512, 849)</td><td>7.0%</td><td>Saunois et al. (2024)</td></tr><tr><td>TD budget</td><td>38.9 (34.6, 43.2)</td><td>575 (553, 586)</td><td>6.8%</td><td></td></tr><tr><td rowspan="2">N<sub>2</sub>O (Tg N<sub>2</sub>O yr<sup>-1</sup>)</td><td>BU budget</td><td>7.2 × 10<sup>-1</sup> (6.5 × 10<sup>-1</sup>, 8.0 × 10<sup>-1</sup>)</td><td>28.6 (16.7, 40.7)</td><td>2.5%</td><td>Tian et al. (2024)</td></tr><tr><td>TD budget</td><td>9.8 × 10<sup>-1</sup> (7.5 × 10<sup>-1</sup>, 1.2)</td><td>27.3 (24.8, 30.2)</td><td>3.6%</td><td></td></tr><tr><td rowspan="2">Net GHG (Tg CO<sub>2</sub>eq yr<sup>-1</sup>)</td><td>BU budget</td><td>4175.2 (4054.8, 4301.3)</td><td>53795.3 (47805.6, 60199.1)</td><td>7.8%</td><td>-</td></tr><tr><td>TD budget</td><td>4292.9 (3760.0, 4825.8)</td><td>-</td><td>-</td><td></td></tr></tbody></table>

Note. The total influence of three GHGs (net GHG budget) was calculated using global warming potentials on 100-year time horizon (GWP100). The GWP100 metrics are adopted from IPCC AR6 WG1 Table 7.15 (Forster et al., 2021). The calculation procedures are detailed in Method Section 2.6.

during 2000–2020, respectively. These increases have been linked to rapid population growth and economic development, which predominantly relies on fossil energy sources such as coal, oil, and natural gas. This relationship has been confirmed in previous research (Ata et al., 2023; M. Hu et al., 2020; Kouyakhi, 2022; J. Li et al., 2019; C. Wang et al., 2020; C. Zhao et al., 2023).

The natural ecosystem part of the CO₂ budget in the CWA region consist of two components: first, CO₂ absorption by terrestrial natural ecosystems, and second, CO₂ emissions from inland waters.

The first component is based on results from multiple TBMs, which provided the spatial and temporal patterns of NBP from 2000 to 2020. The ensemble mean indicates that C sinks were mainly in northern CWA (Georgia, Turkey, Cyprus, Kyrgyzstan, Armenia, Kazakhstan), whereas other countries served as very weak C sinks or C sources. Given the scarcity of studies directly focusing on the CWA NBP, we compared our spatial pattern of NBP with studies focused on the related term NEP (Randerson et al., 2002) in the CWA region, finding our results consistent with the literature (S. Li et al., 2023; Z. Li et al., 2020; Sivrikaya et al., 2013). Notably, we observed no significant changes in the region's NBP during this period. This observation might result from two main factors. First, increased summer drought, caused by increased temperatures and decreased precipitation, limited vegetation growth and consequently decreased the C sink, especially in vegetated areas like northern Kazakhstan from 2000 to 2012 (H. Xu et al., 2016). Second, accelerated reforestation and reduced desertification in Kazakhstan (Chen et al., 2016; S. Li et al., 2023) and Turkey (Sivrikaya et al., 2013) have led to an increase in C sequestration. Overall, the size of the region's C sink did not significantly change, yet the regional anthropogenic emissions continued to increase.

The second component is driven by CO₂ emissions from inland water bodies. These emissions are relatively small compared to neighboring regions—such as South Asia, East Asia, Russia, and Europe, where emissions are 239, 294, 803, and 191 Tg CO₂eq yr⁻¹, respectively, compared to 73 Tg CO₂eq yr⁻¹ in the CWA region—offset nearly one-third of the region's natural C sink (Lauerwald et al., 2023).

Our results underscore the limited capacity of the region's natural ecosystems to sequester C and offset anthropogenic GHG emissions.

### 4.1.2. CH₄ Budget

CH₄ was the second largest component of the CWA GHG budget from 2000 to 2020, contributing 33.4% of the total BU GHG budget and 26.0% of the total TD GHG budget in GWP 100. Given its potent short-term radiative forcing, calculations using GWP20 metrics show that CH₄'s contribution was over 50% of the total, with a fraction of 58.7% using the BU estimate and 50.9% using the TD estimate.
Stavert et al. (2022) reported that the global CH₄ budget increased at an average rate of 0.7% per year (BU) and 0.6% per year (TD) during 2000–2017, relative to the global levels in 2000. Following the same calculation methods and limiting the analysis to 2000–2017, our results indicate that the CH₄ budget in the CWA region increased at an average rate of 1.8% per year (BU) and 1.5% per year (TD) relative to the regional levels in 2000, which were more than double the global average. Extending the analysis to 2000–2020, the CH₄ budget in the CWA region increased at an average rate of 1.5% per year (BU) and 1.3% per year (TD), indicating that the long-term growth rates remained consistent throughout the extended period. Additionally, based on the global CH₄ budget reported by Saunois et al. (2024) for 2010–2019 (estimated at 669 Tg CH₄ yr⁻¹ (BU) and 575 Tg CH₄ yr⁻¹ (TD)), the CWA CH₄ budget constituted 7.0% of the global total in BU estimates and 6.8% in TD estimates over the same period (Table 6). These findings underscore the critical need for targeted emission reductions. According to the BU estimates, the increase in the total CH₄ budget during 2000–2020 was primarily driven by three key sub-sectors. Fugitive emissions from oil and natural gas exploitation increased by 34.3% and accounted for 46.4% of the total CH₄ increase. Emissions from solid waste disposal increased by 111.2% and contributed 20.8% to the total increase. Livestock enteric fermentation emissions increased by 51.5% and contributed 15.3% to the overall CH₄ budget increase. These findings align with the BU evaluation of the Asian CH₄ budget by Ito et al. (2023), which also highlighted that CH₄ emissions from fossil fuel exploitation, waste, and livestock are key targets for emission reductions in mitigation pathways due to their rapid growth and significant contributions to the CWA CH₄ budget increase in recent decades.

Our results further identify the major sources and hot spots of CH₄ increase for 2000–2020 period, providing valuable insights to inform policy design. Specifically, CH₄ emissions from oil and natural gas exploitation were mainly driven by Iraq (46.9%), Iran (22.2%), Qatar (19.0%), and Saudi Arabia (17.2%). Emissions from solid waste disposal were largely driven by Turkey (46.0%), Saudi Arabia (9.5%), Kuwait (6.8%), and Iraq (6.8%). Emissions from livestock enteric fermentation were primarily driven by Uzbekistan (29.5%), Turkey (27.5%), and Kazakhstan (18.2%).

The natural CH₄ budget includes emissions from wetlands, wildfires, inland waters, and geological sources. An ensemble analysis of eleven process-based TBMs indicated a slight increase in wetland CH₄ emissions during 2000–2020, at a rate of 4.0 × 10⁻³ Tg CH₄ yr⁻². Our spatial analysis of wetland CH₄ emission rates and magnitudes aligns with the findings by Zhang et al. (2017) and Ito et al. (2023). However, few studies have explored the causes of this growth. A recent global study by Z. Zhang et al. (2024) indicated that rising temperatures were the primary driver of increased wetland CH₄ emissions in the 30°N–60°N latitude band during 2000–2020, with enhanced precipitation and rising atmospheric CO₂ concentrations playing secondary roles with high levels of uncertainty. Although wetlands in the CWA region fall within this latitude range and have experienced warmer conditions (Z. Hu et al., 2014; Zittis et al., 2022) and rising atmospheric CO₂ concentrations (Cao et al., 2017; Fonseca & Francis, 2024), concurrent droughts (Z. Hu et al., 2019; Pyarali et al., 2022) may have offset some of the increases in wetland CH₄ emissions that would otherwise have been driven by temperature and atmospheric CO₂ concentration. The net effects of these three factors on wetland CH₄ emissions in the CWA region therefore remain uncertain. Future studies could employ factorial simulations using TBMs to isolate and quantify the individual and combined influences of these climatic and environmental drivers, thereby helping to identify the key driver(s) behind the observed increases in wetland CH₄ emissions.

CH₄ emissions from wildfires were small, accounting for less than 1% of the regional total CH₄ budget. In contrast, emissions from inland waters contributed more significantly, comprising 10.5% of the total. In the CWA region, lakes dominate the inland water surface area, making them the primary contributors to regional aquatic CH₄ emissions (Lauerwald et al., 2023). However, research on this significant emission source is limited, highlighting the need for future studies to invest more in long-term in-situ measurements to confirm the magnitude and reveal the underlying mechanisms.

Geological CH₄ emissions also made a notable contribution to the regional total CH₄ budget (12.9%), with onshore micro-seeps from mud volcanoes and diffuse microseepages being the main contributors, consistent with findings from Ito et al. (2023). Emissions from onshore micro-seeps from mud volcanoes were primarily found around the Caspian Sea, particularly in Azerbaijan and Turkmenistan, where some of the world's largest and most active mud volcanoes are located (Dimitrov, 2003; Etiope et al., 2019). Diffuse microseepage emissions were mainly found in Azerbaijan, Iran, Kazakhstan, and Turkmenistan. These findings confirm that a significant portion of drylands lies over sedimentary basins that host natural gas and oil reservoirs, where gas migration to the
surface emits CH₄ into the atmosphere (Etiope & Klusman, 2010; Hosseini et al., 2022; Tang et al., 2010; Umirova et al., 2016).

### 4.1.3. N₂O Budget

In contrast to CO₂ and CH₄ budgets, N₂O contributed only a small portion to the regional GHG budget, accounting for 5.0% of the BU total GHG budget and 6.8% of the TD total GHG budget in GWP100 during 2000–2020. According to recent findings by Tian et al. (2024), the global N₂O budget, encompassing both natural and anthropogenic sources, was estimated to be between 17.4 (TD) and 18.2 (BU) Tg N yr⁻¹ for the decade 2010–2019. Building upon this, our analysis reveals that the CWA region's N₂O emissions constitute 2.5% of the global N₂O budget according to BU estimates, and 3.6% according to TD estimates (Table 6). Although representing only a small portion of regional and global total N₂O emissions, the regional total shows a notable but slow increase, at rates of 9.5 × 10⁻³ (BU) and 1.3 × 10⁻² (TD) Tg N₂O yr⁻². This increase was mainly attributed to emissions caused by exogenous N addition to agricultural soils, the chemical industry, and manure management, contributing 30.5%, 16.1%, and 13.1% to the growth, respectively. Increased crop production to meet the food demands of a growing population in the region was the reson for the rapidly increasing application of synthetic and organic N-fertilizers (Tian et al., 2020, 2022; R. Xu et al., 2020). However, the excessive active N in the soil not only escalated N₂O emissions but also resulted in significant legacy effects, such as water pollution (Galloway et al., 2003; Van Meter et al., 2018). Nonetheless, Tian et al. (2022) highlight that the CWA region can benefit from adopting management practices proven successful in Europe and the USA to curb the rise in anthropogenic N inputs and mitigate local N-induced pollution. This approach, however, might require international collaboration and efficient coordination between developing and developed countries.

The increase in N₂O emissions from the chemical industry was linked to economic development and population growth (Winiwarter et al., 2018). This sector possesses the greatest potential for emission reduction. For instance, previous studies have shown that applying catalytic or thermal reduction techniques, or a combination of both, in adipic acid production could lead to approximately a 75% reduction in N₂O emissions from this industry in Central Asia (Winiwarter et al., 2018). This reduction indicates a significant opportunity for cost-effective emissions abatement within the CWA region.

Petersen et al. (2013) reported that manure management has become a major problem due to more intensive livestock production in Asia and the Pacific region. However, research specifically addressing the CWA region's N₂O increase, and its underlying causes is markedly scarce. Given its significant contribution to regional N₂O emissions growth, more research efforts are needed to quantify subsector-specific emissions, uncover the mechanisms driving these increases, and design targeted mitigation strategies.

The natural N₂O budget was relatively stable from 2000 to 2020. The latest Global N₂O Budget underscored that perturbed fluxes from climate change, atmospheric CO₂ levels, and land cover changes had no significant impact on this region's natural source emissions (Tian et al., 2024).

## 4.2. Limitations in Budget Development and Further Research

### 4.2.1. Limitations in GHG Estimates From Inventory Databases

We integrated four global inventory databases (EDGAR v8, CEDS v2024_04_01, FAOSTAT 2023, GFED v4.1s) to estimate country- and sector-specific GHG emissions (CO₂, CH₄, N₂O) across various anthropogenic activities, including energy, industry, agriculture, and waste sectors. Our results reveal a significant increase in anthropogenic emissions between 2000 and 2020, with CO₂ and CH₄ emissions from fossil fuel combustion and fugitive releases identified as the dominant sources. Aside from GFED v4.1s, which utilized a biogeochemical model forced by data on fire activity, vegetation characteristics, and meteorology to estimate gridded monthly GHG emissions from fires, the other three inventories estimated emissions using activity data and EFs (Crippa et al., 2023; Hoesly et al., 2018; Tubiello et al., 2021, 2022; Van Der Werf et al., 2017).

A closer investigation of the sources of activity data reveals that the inventories mainly based on data from international organizations (e.g., the International Energy Agency (IEA), the Global Gas Flaring Reduction Partnership of the World Bank (GGFR), and the International Air Transport Association (IATA)) as well as global databases and reports (e.g., PRIMAP data (Gütschow et al., 2021), the Global Wildfire Data set under the GWIS (Artés et al., 2019), and the Energy Institute Statistical Review of World Energy (EI, 2023)). Although these
sources provide full coverage of CWA countries, the lack of data provided by local government statistical offices introduces certain limitations, such as delays in updates and incomplete coverage of country-specific activities (Solazzo et al., 2021). Regarding EFs, the inventories follow Tier 1 methodologies outlined in the IPCC Guidelines for National Greenhouse Gas Inventories (Crippa et al., 2023; Hoesly et al., 2018; Tubiello et al., 2021, 2022). This approach employs default EFs that are globally averaged, not fully considering local variations such as the specific characteristics of industrial processes, fuel types, or agricultural practices unique to the CWA region.

To address these limitations, we recommend that CWA countries track and report emissions from their oil, gas, and coal production and transmission, as well as ultra-emitter sites, using new tools such as moderate- and high-resolution satellite imagery (Deng et al., 2022). Local research institutes, universities, and even energy producers could actively collaborate with international organizations to secure essential funding and technical expertise for collecting and sharing localized activity data. For EFs, we suggest utilizing values from the IPCC Emission Factor Database (EFDB), which contains EFs specific to different countries, regions, and activities, that are verified by experts (Ogle et al., 2013). Improving the representativeness of both activity data and EFs for the CWA region could help to enhance the robustness of GHG inventories.

### 4.2.2. Uncertainties in GHG Estimates From Atmospheric Inversions

The ensemble means of five inversions from the GCB project, five from the GMB project, and four from the GNB project were used as the TD constraints for the CO₂, CH₄, and N₂O budgets, respectively. Figure S2 in Supporting Information S1 illustrates the temporal changes of each inversion at the regional level for the three GHGs. The trends are generally consistent, but there are discrepancies in total magnitude. Figure S3 in Supporting Information S1 further illustrates the spatial distribution of uncertainties for CO₂, CH₄, and N₂O inversions. For CO₂, the uncertainties are primarily concentrated in high-emission regions along the Persian Gulf, including eastern Saudi Arabia, Kuwait, central Iraq, and eastern Iran, likely driven by fossil fuel combustion and industrial activities. For CH₄, the uncertainties are mainly located in regions with intensive fossil fuel extraction and processing activities, such as the Persian Gulf and eastern Turkmenistan. For N₂O, the uncertainties are mainly observed in eastern Afghanistan, western Syria, and Lebanon, likely associated with fertilizer use and soil management practices. Uncertainties from inversions in the CWA region could be mainly attributed to the limited availability of atmospheric observations and the coarse resolution. Upon examining all accessible observations utilized by these inversions (Chevallier et al., 2005; Deng et al., 2022; Niwa et al., 2022; Remaud et al., 2018; Rodenbeck et al., 2003; Tian et al., 2024; Tibrewal et al., 2024), it is evident that only one or two stations are located in the CWA region for measuring CO₂, CH₄ and N₂O concentrations. Moreover, global inversions operating at a relatively coarse resolution may misallocate emissions from neighboring countries, leading to additional uncertainties for smaller countries in the region.

To address the scarcity of observational data across the CWA region, it is crucial to establish additional ground-based observational stations. Optimal locations for new stations can be identified using a Lagrangian Particle Dispersion Model (LPDM) in reverse mode, combined with a Bayesian inverse modeling framework (Ziehn et al., 2014). This approach identifies high-priority sites that could significantly reduce uncertainties in emission estimates by incorporating contributions from existing station observations and prior emission estimates. This method has been successfully applied to optimize GHG monitoring networks in Australia (Ziehn et al., 2016), South Africa (Nickless et al., 2015), and across Africa (Nickless et al., 2020). Measuring instruments at the new sites are recommended to be automated to enable long-term continuous observations, with targeted precision levels of 0.1 ppm for CO₂, 2 ppb for CH₄, and 0.1 ppb for N₂O (Semerjian et al., 2024). The temporal resolution should vary based on the site type: for background stations, hourly or daily averages are appropriate, while for emission hotspots, minute-level averages are preferred (WMO, 2019). Such measurements can help understand the diurnal and seasonal patterns of GHGs, as well as capture accidental releases and help regulators assessing progress toward GHG emission mitigation targets. Moreover, the observations meet WMO/GAW data quality standards, allowing them to be incorporated into international networks and used by inversion systems. In addition, considering the political issues and economic challenges in the CWA countries, collaboration between local research institutions, universities, and government bodies with international organizations, such as the World Meteorological Organization, the Environment Facility of the World Bank, and the Green Climate Fund is essential. Such partnerships can provide both financial and technical support for the scientific selection of sites, as
well as the buildup, operation, management, and monitoring in these stations (Henne et al., 2008; Mostefaoui et al., 2024).

In addition to establishing more observational stations to better constrain the inversions, a further assessment of the atmospheric chemistry and transport models used by inversion systems could help reduce uncertainties in the inversion-based TD budgets, particularly the CH₄ budgets. Using a variety of atmospheric chemistry and transport models in inversion systems has been demonstrated as a driving factor contributing to uncertainties in inversion estimates at global scales (Gaubert et al., 2019; Saunois et al., 2024; Schuh et al., 2019). For instance, different hydroxyl radical (OH) fields derived from Chemistry Transport Models (CTM) and Climate Chemistry Models (CCM) directly impact CH₄ sink estimates and thus contribute to inversion uncertainties (Nicely et al., 2017; Y. Zhao et al., 2019). Additionally, CH₄ inversion estimates are sensitive to factors such as the troposphere-stratosphere exchange rate and the vertical grid structure in transport processes, which significantly contribute to uncertainties in inversions (Patra et al., 2011). However, the extent to which these key processes affect the uncertainties in inversions for the CWA region remains unclear. Thus, at the regional level, evaluating the atmospheric chemistry and transport models is crucial to identify shortcomings and improve atmospheric modeling (e.g., robust representation of OH fields, finer horizontal and vertical resolutions, and more accurate physical parameterization) to further reduce uncertainties.

### 4.2.3. Uncertainties in GHG Estimates From Process-Based Terrestrial Biosphere Models

In this study, we used results from process-based TBMs to estimate the natural ecosystems' NBP (TRENDY), natural wetland CH₄ emissions (GMB), and natural soil N₂O emissions (NMIP2). All models within each framework used the same driving data, so the uncertainties primarily arise from differences in model configuration and parameterization.

The TRENDY models exhibited similar interannual variability but with varying magnitudes (Figure S1a in Supporting Information S1). The distribution of uncertainties shows that larger uncertainties were present not only in densely vegetated areas like Georgia and North Kazakhstan, but also in sparsely vegetated regions like Turkmenistan, southern Afghanistan, central and southwest Saudi Arabia, and western Yemen (Figure 4d). This may suggest that the models are limited in their ability to comprehensively and accurately simulate complex plant-soil-water interactions across various land covers in the semi-arid and arid regions. Previous studies suggest that unlike most temperate regions of the Northern Hemisphere, where vegetation growth has been stimulated by warming, the effects of warming and CO₂ fertilization are limited or negated in Central Asia due to drought stress (Zhang & Ren, 2017). This is corroborated by field experiments, which found that although summer storms temporarily boost productivity, the resulting increase in vapor pressure deficit (drier air) reduces overall plant productivity (Roby et al., 2020). Thus, a more detailed analysis of C flux and plant hydraulic processes, aligned with field observations, is crucial to enhance the models' accuracy and realistically simulate patterns for the CWA region.

The GMB TBMs showed diverging temporal changes between 2000 and 2020 (Saunois et al., 2024). Four models presented slight but significant increasing trends, while others did not (Figure S1b in Supporting Information S1). Differences among model results were mainly observed in regions with higher CH₄ emissions (Figure 7d). Ito et al. (2023) reported a small seasonal variation in wetland CH₄ emissions for the CWA region, with emissions tending to be higher in the summer. This suggests that wetland CH₄ emissions in CWA are regulated by time-varying environmental factors such as temperature, precipitation, evapotranspiration, and soil moisture, and model discrepancies may be due to varying responses to these factors. However, limited research makes it unclear what the primary driver(s) are and how the combined effects of these factors on wetland CH₄ emissions in the CWA region. To reduce this uncertainty, future research could focus on conducting more field-based measurements and improving the model representation of wetland CH₄ emissions in response to these factors. This would help provide more robust estimates of CH₄ emissions in the CWA region.

The NMIP2 models exhibited different N₂O emission magnitudes and interannual variations from 2000 to 2020 (Figure S1c in Supporting Information S1), with uncertainties mainly in regions with higher N₂O emissions (Figure 10d). The soil N₂O emissions are primarily produced by microbial nitrification and denitrification, regulated by factors like substrate concentration, precipitation, temperature, pH, vegetation type, and atmospheric CO₂ (Tian et al., 2020, 2024). Divergence among NMIP2 estimates likely results from varied responses to these factors. While few field studies have focused on CWA N₂O emissions, some experiments in Xinjiang, China
(close to our study area) indicated that external N input (e.g., atmospheric deposition, fertilizers, manure, etc.) could increase N₂O emissions in Central Asia (Geng et al., 2019; Zhou et al., 2019). The effects of other environmental factors and their interactions on N₂O emissions remain poorly understood, and additional field experiments are required to clarify how ongoing global changes and their interactions impact N₂O emissions in the CWA region.

#### 4.2.4. Additional Limitations in the Budgets and Next Steps

We recognize that several lateral fluxes were missing from this budget due to a lack of data, specifically the export of particulate and dissolved organic and inorganic C from inland waters to estuaries (e.g., total C, particulate organic C, particulate inorganic C, dissolved organic C, dissolved inorganic C). It is also important to assess the lateral fluxes of CO₂ and embedded C associated with products that are traded (e.g., fossil fuels, plastic, wood, agricultural products). Ciais et al. (2022) shows that C can be lost through river export or gained through the trade of agricultural products, as well as through the atmospheric transport and deposition of C particles emitted with dust from arid ecosystems. We were unable to find suitable independent inventories or studies for these lateral CO₂ fluxes. Future assessments could calculate lateral CO₂ transport by crop and wood product trade, and by rivers, following the methods outlined by Deng et al. (2022).

A recent study by Rosentreter et al. (2023) reported the absorption and emissions of GHGs from coastal vegetation and estuaries by recalculating the estuarine and coastal vegetation surface area and compiling in-situ observations of GHG fluxes. However, due to the limited availability of observations (fewer than three sites for each gas) in the CWA region, the results are associated with higher uncertainties and were not included here. For future assessments, we may consider using transfer learning algorithms. These algorithms would involve training models on global data sets as a starting point and then applying the model's knowledge to regions with fewer in-situ observations. Including these emissions in future assessments would help to build a more comprehensive and detailed GHG budget.

In addition to including the missing fluxes, our results suggest three key research needs to improve the estimation of GHG budgets in the CWA region. First, the databases and models used in this study are primarily derived from global projects. It would be beneficial to incorporate more national efforts that include in situ observations and regional assessments. Second, integrating TROPOMI satellite data with a high-resolution dispersion model could help estimate ultra-emissions in energy-producing countries as a complement to the inventories. The advent of a number of GHG satellites will facilitate this process shortly. Lastly, it is crucial for TBMs to include results from factorial experiments to quantify the individual and combined effects of ongoing global changes—such as climate change, atmospheric CO₂ concentration, land use change, N deposition, and agricultural N application (fertilizer and manure)—on GHG emissions in the CWA region.

### 5. Conclusions

This study presents the first comprehensive budgets of three important GHGs (CO₂, CH₄, and N₂O) and their synthesis for the CWA region for 2000–2020, utilizing a dual constraint approach. Our results indicate that the CWA region was a significant and growing net source of GHGs during 2000–2020, with net emissions of 4,175 (4,055–4,301) Tg CO₂eq yr⁻¹ based on the BU approach, and 4,293 (3,760–4,826) Tg CO₂eq yr⁻¹ based on the TD approach during the decade 2010–2019. This net GHG source was primarily driven by large emissions from fossil fuel combustion and fugitive emissions, coupled with a very weak land CO₂ sink, which was further offset by natural CH₄ and N₂O emissions. Over this period, the CWA region accounted for 9.6% (BU) of the global CO₂ budget, 7.0% (BU) and 6.8% (TD) of the global CH₄ budget, and 2.5% (BU) and 3.6% (TD) of the global N₂O budget. Moreover, the growth rate of CH₄ in the CWA region was twice faster than the global average, highlighting the oil and natural gas production and processing sectors as priorities for climate mitigation efforts in the region. While this study has filled a critical gap in understanding the GHG budgets of the CWA region, it also identified key uncertainties arising from data limitations and the lack of region-specific modeling and observations. Addressing these uncertainties in future research is essential to refine GHG estimates and support more effective climate mitigation strategies in the CWA region.
## Data Availability Statement

The CO₂, CH₄, N₂O, GHG (with GWP100) and GHG (with GWP20) data used in this study, as well as the scripts for reproducing the figures, are available at Zenodo (Qin & Tian, 2024) via [https://zenodo.org/records/14566089], where they are freely accessible. The Central and West Asia region mask file for the analysis within RECCAP2 is available from the data repository of RECCAP2 (Max-Planck-Institute for Biogeochemistry (MPI-BGC), 2022). The NBP fluxes, wetland CH₄ emissions, and soil N₂O emissions data are available from Friedlingstein et al. (2022), Saunois et al. (2024) and Martinez et al. (2024), and Tian et al. (2018, 2024), respectively. The CO₂, CH₄, N₂O inversion results are available from Friedlingstein et al. (2022) and Luijkx et al. (2023), Saunois et al. (2024) and Martinez et al. (2024), and Tian et al. (2024), respectively. The atmospheric CO₂ observations used by the CO₂ inversions are from the CO₂ ObsPack data sets (Schuldt, Mund, et al., 2021; Schuldt et al., 2022), WDCGG (JMA, 2022), and ICOS ATC (CAMS, 2022). The atmospheric CH₄ observations used by the CH₄ inversions are from the CH₄ ObsPack data sets (Schuldt, Aalto, et al., 2021; Schuldt, Jacobson, et al., 2021), multiple networks including NOAA (Lan et al., 2024; Steele et al., 1987), CSIRO (Francey et al., 1999), AGAGE (Cunnold et al., 2002; Prinn et al., 2018), and individual stations (Saunois et al., 2024). The atmospheric N₂O observations used by the N₂O inversions are from multiple networks including NOAA/GML (Dutton et al., 2024), AGAGE (Prinn et al., 2022), CSIRO (Francey et al., 2003), and individual stations (Tian et al., 2024). Additional data used for our study are from: Crippa et al. (2023) for EDGAR v8 data set; Hoesly and Smith (2024) for CEDS v2024_04_01 data set; Tubiello et al. (2021, 2022) for FAOSTAT database; Van Der Werf et al. (2017) for GFED v4.1s data set; Lauerwald et al. (2023) for inland water GHG emissions, and Etiope et al. (2019) for geological seepages. The analysis was conducted through R version 4.4.1 (R Core Team, 2024). Maps and figures were created using the ggplot2 package version 3.5.1 (Wickham, 2016), rworldmap package (South, 2011), and Adobe Illustrator version 2024 (Adobe Inc, 2024).

## Acknowledgments

This study is part of the Second Regional Carbon Cycle Assessment and Processes-2 (RECCAP2) of the Global Carbon Project, partially supported by the Center for Earth System Science and Global Sustainability and GCP Boston Office at Boston College. We are deeply thankful to Dr. Ingrid Luijkx at Wageningen University & Research for providing atmospheric CO₂ inversions; Dr. Zhen Zhang at the Institute of Tibetan Plateau Research, Chinese Academy of Sciences for providing the wetland CH₄ flux data; Dr. Adrien Martinez at the Laboratoire des Sciences du Climat et de l'Environnement (LSCE) for providing the atmospheric CH₄ inversions; Dr. Rona Thompson for providing the atmospheric N₂O inversions. We would also acknowledge TRENDY participants for making the terrestrial C flux data available for terrestrial C budget synthesis and the NMIP2 participants for making the soil N₂O emission data available for terrestrial N₂O budget synthesis for this study.

## References

* Adobe Inc. (2024). Adobe illustrator [Software]. Adobe Inc. Retrieved from https://www.adobe.com/products/illustrator.html

* Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. *Science*, **361**(6402), 585–588. https://doi.org/10.1126/science.aat0636

* Al-Zahrani, K. H., Baig, M. B., & Straquadine, G. S. (2019). Food waste in the Kingdom of Saudi Arabia: Implications for extension education. In M. Behnassi, O. Pollmann, & H. Gupta (Eds.), *Climate Change, Food Security and Natural resource Management* (pp. 73–101). Springer International Publishing. https://doi.org/10.1007/978-3-319-97091-2_4

* Artés, T., Oom, D., De Rigo, D., Durrant, T. H., Maianti, P., Libertà, G., & San-Miguel-Ayanz, J. (2019). A global wildfire dataset for the analysis of fire regimes and fire behaviour. *Scientific Data*, **6**, 296. https://doi.org/10.1038/s41597-019-0312-2

* Ata, B., Pakrooh, P., & Pénzes, J. (2023). Driving factors of energy related CO₂ emissions at a regional level in the residential sector of Iran. *Scientific Reports*, **13**, 17598. https://doi.org/10.1038/s41598-023-44975-x

* Baig, M. B., Qureshi, A. M., Straquadine, G. S., & Hajiyev, A. (2019). Realizing food security through sustainable agriculture in the Republic of Yemen: Implications for rural extension. In M. Behnassi, O. Pollmann, & H. Gupta (Eds.), *Climate Change, Food Security and Natural Resource Management* (pp. 19–60). Springer International Publishing. https://doi.org/10.1007/978-3-319-97091-2_2

* Barandun, M., Fiddes, J., Scherler, M., Mathys, T., Saks, T., Petrakov, D., & Hoelzle, M. (2020). The state and future of the cryosphere in Central Asia. *Water Security*, **11**, 100072. https://doi.org/10.1016/j.wasec.2020.100072

* Canadell, J. G., Ciais, P., Gurney, K., Le Quéré, C., Piao, S., Raupach, M. R., & Sabine, C. L. (2011). An international effort to quantify regional carbon fluxes. *Eos, Transactions American Geophysical Union*, **92**(10), 81–82. https://doi.org/10.1029/2011EO100001

* Cao, L., Chen, X., Zhang, C., Kurban, A., Yuan, X., Pan, T., & De Maeyer, P. (2017). The temporal and spatial distributions of the near-surface CO₂ concentrations in Central Asia and analysis of their controlling factors. *Atmosphere*, **8**(5), 85. https://doi.org/10.3390/atmos8050085

* Chandra, N., Patra, P. K., Bisht, J. S. H., Ito, A., Umezawa, T., Saigusa, N., et al. (2021). Emissions from the oil and gas sectors, coal mining and ruminant farming drive methane growth over the past three decades. *Journal of the Meteorological Society of Japan*, **99**(2), 309–337. https://doi.org/10.2151/jmsj.2021-015

* Chen, Y., Luo, G., Maisupova, B., Chen, X., Mukanov, B. M., Wu, M., et al. (2016). Carbon budget from forest land use and management in Central Asia during 1961–2010. *Agricultural and Forest Meteorology*, **221**, 131–141. https://doi.org/10.1016/j.agrformet.2016.02.011

* Chevallier, F., Fisher, M., Peylin, P., Serrar, S., Bousquet, P., Bréon, F.-M., et al. (2005). Inferring CO₂ sources and sinks from satellite observations: Method and application to TOVS data. *Journal of Geophysical Research*, **110**, D24309. https://doi.org/10.1029/2005JD006390

* Ciais, P., Bastos, A., Chevallier, F., Lauerwald, R., Poulter, B., Canadell, J. G., et al. (2022). Definitions and methods to estimate regional land carbon fluxes for the second phase of the REgional Carbon Cycle Assessment and Processes Project (RECCAP-2). *Geoscientific Model Development*, **15**(3), 1289–1316. https://doi.org/10.5194/gmd-15-1289-2022

* Copernicus Atmosphere Monitoring Service (CAMS). (2022). Global evaluation data [Dataset]. Copernicus Atmosphere Monitoring Service (CAMS). Retrieved from https://global-evaluation.atmosphere.copernicus.eu/

* Crippa, M., Guizzardi, D., Pagani, F., Banja, M., Muntean, M., Schaaf, E., et al. (2023). GHG emissions of all world countries (No. JRC134504). Publications Office of the European Union. https://doi.org/10.2760/953322

* Cunnold, D. M., Steele, L. P., Fraser, P. J., Simmonds, P. G., Prinn, R. G., Weiss, R. F., et al. (2002). In situ measurements of atmospheric methane at GAGE/AGAGE sites during 1985–2000 and resulting source inferences. *Journal of Geophysical Research*, **107**(D14), 4225. https://doi.org/10.1029/2001JD001226

* Dangal, S. R. S., Tian, H., Xu, R., Chang, J., Canadell, J. G., Ciais, P., et al. (2019). Global nitrous oxide emissions from pasturelands and rangelands: Magnitude, spatiotemporal patterns, and attribution. *Global Biogeochemical Cycles*, **33**(2), 200–222. https://doi.org/10.1029/2018GB006091
Dastorani, M., & Jafari, M. (2019). Analysis of the trend of dust changes in Ardestan Region, Iran. *Desert Ecosystem Engineering Journal*, 8(3), 45–54. https://doi.org/10.22052/jdee.2019.173755.1049

Deng, Z., Ciais, P., Tzompa-Sosa, Z. A., Saunois, M., Qiu, C., Tan, C., et al. (2022). Comparing national greenhouse gas budgets reported in UNFCCC inventories against atmospheric inversions. *Earth System Science Data*, 14(4), 1639–1675. https://doi.org/10.5194/essd-14-1639-2022

Dimitrov, L. I. (2003). Mud volcanoes-a significant source of atmospheric methane. *Geo-Marine Letters*, 23, 155–161. https://doi.org/10.1007/s00367-003-0140-3

Dutton, G., Hall, B., Dlugokencky, E., Lan, X., Nance, D., Gentry, M., et al. (2024). Combined atmospheric nitrous oxide dry air mole fractions from the NOAA GML halocarbons sampling network, 1977-2024 (version 2024-02-21) [Dataset]. *NOAA GML*. https://doi.org/10.15138/GMZ7-2Q16

Energy Institute (EI). (2023). 2023 statistical review of world energy [Dataset]. *Energy Institute (EI)*. Retrieved from https://www.energyinst.org/statistical-review

Etiope, G., Ciotoli, G., Schwietzke, S., & Schoell, M. (2019). Gridded maps of geological methane emissions and their isotopic signature. *Earth System Science Data*, 11(1), 1–22. https://doi.org/10.5194/essd-11-1-2019

Etiope, G., & Klusman, R. W. (2010). Microseepage in drylands: Flux and implications in the global atmospheric source/sink budget of methane. *Global and Planetary Change*, 72(4), 265–274. https://doi.org/10.1016/j.gloplacha.2010.01.002

Feng, L., Palmer, P. I., Parker, R. J., Deutscher, N. M., Feist, D. G., Kivi, R., et al. (2016). Estimates of European uptake of CO₂ inferred from GOSAT XCO₂ retrievals: Sensitivity to measurement bias inside and outside Europe. *Atmospheric Chemistry and Physics*, 16(3), 1289–1302. https://doi.org/10.5194/acp-16-1289-2016

Fonseca, R., & Francis, D. (2024). Satellite derived trends and variability of CO₂ concentrations in the Middle East during 2014–2023. *Frontiers in Environmental Science*, 11, 1289142. https://doi.org/10.3389/fenvs.2023.1289142

Food and Agriculture Organization of the United Nations (FAO). (2023). FAO statistical database: Land Use [Dataset]. *FAOSTAT*. Retrieved from https://www.fao.org/faostat/en/#data/RL

Forster, P. M., Smith, C. J., Walsh, T., Lamb, W. F., Lamboll, R., Hauser, M., et al. (2023). Indicators of Global Climate Change 2022: Annual update of large-scale indicators of the state of the climate system and human influence. *Earth System Science Data*, 15(6), 2295–2327. https://doi.org/10.5194/essd-15-2295-2023

Forster, P. M., Storelymo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., et al. (2021). The Earth's energy budget, climate feedbacks and climate sensitivity. In *Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change* (pp. 923–1054). Cambridge University Press. Retrieved from. https://doi.org/10.1017/9781009157896.009

Francey, R. J., Steele, L. P., Langenfelds, R. L., & Pak, B. C. (1999). High precision long-term monitoring of radiatively active and related trace gases at surface sites and from aircraft in the Southern Hemisphere atmosphere. *Journal of the Atmospheric Sciences*, 56(2), 279–285. https://doi.org/10.1175/1520-0469(1999)056%3C0279:HPLTMO%3E2.0.CO;2

Francey, R. J., Steele, L. P., Spencer, D. A., Langenfelds, R. L., Law, R. M., Krummel, P. B., et al. (2003). *The CSIRO (Australia) measurement of greenhouse gases in the global atmosphere (report of the eleventh WTO/IAEA meeting of experts on carbon dioxide concentration and related tracer measurement techniques No. 148)* (pp. 97–111). World Meteorological Organization Global Atmosphere Watch. Retrieved from https://library.wmo.int/idurl/4/41199

Frey, C., Penman, J., Hanle, L., Monni, S., & Ogle, S. (2006). Chapter 3: Uncertainties. In 2006 IPCC Guidelines for National Greenhouse Gas Inventories, General guidance and reporting (Vol. 1). Retrieved from https://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/1_Volume1/V1_3_Ch3_Uncertainties.pdf

Friedl, M., & Sulla-Menashe, D. (2022). MODIS/Terra+Aqua land cover type yearly L3 global 0.05Deg CMG V061 [Dataset]. *NASA EOSDIS Land Processes Distributed Active Center*. Retrieved from https://doi.org/10.5067/MODIS/MCD12C1.061

Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Bakker, D. C. E., Hauck, J., et al. (2023). Global carbon budget 2023. *Earth System Science Data*, 15(12), 5301–5369. https://doi.org/10.5194/essd-15-5301-2023

Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Gregor, L., Hauck, J., et al. (2022). Global carbon budget 2022. *Earth System Science Data*, 14(11), 4811–4900. https://doi.org/10.5194/essd-14-4811-2022

Galloway, J. N., Aber, J. D., Erisman, J. W., Seitzinger, S. P., Howarth, R. W., Cowling, E. B., & Cosby, B. J. (2003). The nitrogen cascade. *BioScience*, 53(4), 341–356. https://doi.org/10.1641/0006-3568(2003)053[0341:TNC]2.0.CO;2

Gaubert, B., Stephens, B. B., Basu, S., Chevallier, F., Deng, F., Kort, E. A., et al. (2019). Global atmospheric CO₂ inverse models converging on neutral tropical land exchange, but disagreeing on fossil fuel and atmospheric growth rate. *Biogeosciences*, 16(1), 117–134. https://doi.org/10.5194/bg-16-117-2019

Gelaro, R., Mccarty, W., Su, M. J., Todling, R., Molod, A., Takacs, L., et al. (2017). The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). *Journal of Climate*, 30(14), 5419–5454. https://doi.org/10.1175/JCLI-D-16-0758.1

Geng, F., Li, K., Liu, X., Gong, Y., Yue, P., Li, Y., & Han, W. (2019). Long-term effects of N deposition on N₂O emission in an alpine grassland of Central Asia. *Catena*, 182, 104100. https://doi.org/10.1016/j.catena.2019.104100

GOSAT Project. (2011). Greenhouse gases observing satellite “IBUKI” (GOSAT) [Dataset]. *GOSAT Project*. Retrieved from https://www.gosat.nies.go.jp/en/

Gütschow, J., Jeffery, L., & Gieseke, R. (2021). The PRIMAP-hist national historical emissions time series v2.3 (1850–2017) [Dataset]. Retrieved from https://doi.org/10.5880/pik.2019.001

Harada, Y., Kamahori, H., Kobayashi, C., Endo, H., Kobayashi, S., Ota, Y., et al. (2016). The JRA-55 reanalysis: Representation of atmospheric circulation and climate variability. *Journal of the Meteorological Society of Japan*, 94(3), 269–302. https://doi.org/10.2151/jmsj.2016-015

Henne, S., Junkermann, W., Kariuki, J. M., Aseyo, J., & Klausen, J. (2008). Mount Kenya Global Atmosphere Watch Station (MKN): Installation and meteorological characterization. *Journal of Applied Meteorology and Climatology*, 47(11), 2946–2962. https://doi.org/10.1175/2008JAMC1834.1

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., et al. (2020). The ERA5 global reanalysis. *Quarterly Journal of the Royal Meteorological Society*, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803

Hoesly, R. M., & Smith, S. J. (2024). CEDS v_2024_04_01 release emission data (version v_2024_04_01) [Dataset]. *Zenodo*. Retrieved from https://doi.org/10.5281/zenodo.10904361

Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., et al. (2018). Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS). *Geoscientific Model Development*, 11(1), 369–408. https://doi.org/10.5194/gmd-11-369-2018
Hosseini, A., Saberi, M. H., & ZareNezhad, B. (2022). Significance of petroleum seepages in hydrocarbon exploration-case study of Khourian Desert, Central Iran. *Journal of Petroleum Exploration and Production Technology, 12*, 1649–1663. https://doi.org/10.1007/s13202-021-01440-7

Hu, M., Li, R., You, W., Liu, Y., & Lee, C.-C. (2020). Spatiotemporal evolution of decoupling and driving forces of CO₂ emissions on economic growth along the Belt and Road. *Journal of Cleaner Production*, 277(20), 123272. https://doi.org/10.1016/j.jclepro.2020.123272

Hu, Z., Chen, X., Chen, D., Li, J., Wang, S., Zhou, Q., et al. (2019). “Dry gets drier, wet gets wetter”: A case study over the arid regions of central Asia. *International Journal of Climatology*, 39(2), 1072–1091. https://doi.org/10.1002/joc.5863

Hu, Z., Zhang, C., Hu, Q., & Tian, H. (2014). Temperature changes in Central Asia from 1979 to 2011 based on multiple datasets. *Journal of Climate*, 27(3), 1143–1167. https://doi.org/10.1175/JCLI-D-13-00064.1

Hurtt, G. C., Chini, L. P., Frolking, S., Betts, R. A., Feddema, J., Fischer, G., et al. (2011). Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. *Climatic Change*, 109, 117–161. https://doi.org/10.1007/s10584-011-0153-2

Intergovernmental Panel on Climate Change (IPCC). (2021). Annex VII: Glossary. In *Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change* (pp. 2215–2256). Cambridge University Press. Retrieved from. https://doi.org/10.1017/9781009157896.022

Intergovernmental Panel On Climate Change (IPCC). (2022). In *Climate Change 2022—Mitigation of Climate Change: Working Group III Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change*. Cambridge University Press. Retrieved from https://www.ipcc.ch/report/ar6/wg3/

International Monetary Fund (IMF). (2024). *Regional economic outlook, Middle East and Central Asia, April 2024: An uneven recovery amid high uncertainty*. International Monetary Fund. https://doi.org/10.5089/9798400272356.086

Ito, A., Nishina, K., Ishijima, K., Hashimoto, S., & Inatomi, M. (2018). Emissions of nitrous oxide (N₂O) from soil surfaces and their historical changes in East Asia: A model-based assessment. *Progress in Earth and Planetary Science*, 5, 55. https://doi.org/10.1186/s40645-018-0215-4

Ito, A., Patra, P. K., & Umezawa, T. (2023). Bottom-up evaluation of the methane budget in Asia and its subregions. *Global Biogeochemical Cycles*, 37(6), e2023GB007723. https://doi.org/10.1029/2023GB007723

Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Dentener, F., et al. (2019). EDGAR v4.3.2 global atlas of the three major greenhouse gas emissions for the period 1970–2012. *Earth System Science Data, 11*(3), 959–1002. https://doi.org/10.5194/essd-11-959-2019

Japan Meteorological Agency (JMA). (2022). WMO World Data Centre for Greenhouse Gases (WDCGG) [Dataset]. *Japan Meteorological Agency (JMA)*. Retrieved from https://gaw.kishou.go.jp/

Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., et al. (2015). The JRA-55 reanalysis: General specifications and basic characteristics. *Journal of the Meteorological Society of Japan*, 93(1), 5–48. https://doi.org/10.2151/jmsj.2015-001

Kouyakhi, N. R. (2022). CO₂ emissions in the Middle East: Decoupling and decomposition analysis of carbon emissions, and projection of its future trajectory. *Science of the Total Environment*, 845, 157182. https://doi.org/10.1016/j.scitotenv.2022.157182

Lan, X., Thoning, K. W., & Dlugokencky, E. J. (2024). Trends in globally-averaged CH₄, N₂O, and SF₆ determined from NOAA Global Monitoring Laboratory measurements (version 2024-11) [Dataset]. *National Oceanic and Atmospheric Administration, GML*. https://doi.org/10.15138/PXG-AA10

Lauerwald, R., Allen, G. H., Deemer, B. R., Liu, S., Maavara, T., Raymond, P., et al. (2023). Inland water greenhouse gas budgets for RECCAP2: 2. Regionalization and homogenization of estimates. *Global Biogeochemical Cycles*, 37(5), e2022GB007658. https://doi.org/10.1029/2022GB007658

Lauvaux, T., Giron, C., Mazzolini, M., Shindell, D., Ciais, P., Cusworth, D., et al. (2022). Global assessment of oil and gas methane ultra-emitters. *Science*, 375(6580), 557–561. https://doi.org/10.1126/science.abj4351

Li, J., Chen, Y., Li, Z., & Huang, X. (2019). Low-carbon economic development in Central Asia based on LMDI decomposition and comparative decoupling analyses. *Journal of Arid Land, 11*, 513–524. https://doi.org/10.1007/s40333-019-0063-0

Li, S., He, S., Xu, Z., Liu, Y., & Von Bloh, W. (2023). Desertification process and its effects on vegetation carbon sources and sinks vary under different aridity stress in Central Asia during 1990–2020. *Catena*, 221, 106767. https://doi.org/10.1016/j.catena.2022.106767

Li, Z., Chen, Y., Zhang, Q., & Li, Y. (2020). Spatial patterns of vegetation carbon sinks and sources under water constraint in Central Asia. *Journal of Hydrology*, 590, 125355. https://doi.org/10.1016/j.jhydrol.2020.125355

Luijkx, I. T., Chevallier, F., Roedenbeck, C., Niwa, Y., Liu, J., Feng, L., et al. (2023). Global CO₂ gridded flux fields from 9 atmospheric inversions in GCB2022 [Dataset]. Retrieved from https://doi.org/10.18160/7AH8-K1X4

Maksyutov, S., Oda, T., Saito, M., Janardanan, R., Belikov, D., Kaiser, J. W., et al. (2021). Technical note: A high-resolution inverse modelling technique for estimating surface CO₂ fluxes based on the NIES-TM-FLEXPART coupled transport model and its adjoint. *Atmospheric Chemistry and Physics*, 21(2), 1245–1266. https://doi.org/10.5194/acp-21-1245-2021

Martinez, A., Saunois, M., Poulter, B., Zhen, Z., Raymond, P., Regnier, P., et al. (2024). Supplemental data of the Global Carbon Project Methane Budget 2024 v1 [Dataset]. Retrieved from https://doi.org/10.18160/GKQ9-2RHT

Mathews, S. (2022). Profits soar at Middle East fertiliser companies amid Ukraine war. Retrieved from https://www.middleeasteye.net/news/russia-ukraine-war-middle-east-fertiliser-booms-supply-shocks

Max-Planck-Institute for Biogeochemistry (MPI-BGC). (2022). Data exchange portal [Dataset]. *MPI-BGC*. Retrieved from https://www.bgc-jena.mpg.de/geodb/projects/Home.php

Messager, M. L., Lehner, B., Grill, G., Nedeva, I., & Schmitt, O. (2016). Estimating the volume and age of water stored in global lakes using a geo-statistical approach. *Nature Communications*, 7, 13603. https://doi.org/10.1038/ncomms13603

Mostefaoui, M., Ciais, P., McGrath, M. J., Peylin, P., Patra, P. K., & Ernst, Y. (2024). Greenhouse gas emissions and their trends over the last 3 decades across Africa. *Earth System Science Data*, 16(1), 245–275. https://doi.org/10.5194/essd-16-245-2024

Murray-Tortarolo, G., Perea, K., Mendoza-Ponce, A., Martínez-Arroyo, A., Murguía-Flores, F., Jaramillo, V. J., et al. (2024). A greenhouse gas budget for Mexico during 2000–2019. *Journal of Geophysical Research: Biogeosciences*, 129(1), e2023JG007667. https://doi.org/10.1029/2023JG007667

Nicely, J. M., Salawitch, R. J., Canty, T., Anderson, D. C., Arnold, S. R., Chipperfield, M. P., et al. (2017). Quantifying the causes of differences in tropospheric OH within global models. *Journal of Geophysical Research*, 122(3), 1983–2007. https://doi.org/10.1002/2016JD026239

Nickless, A., Scholes, R. J., Vermeulen, A., Beck, J., López-Ballesteros, A., Ardö, J., et al. (2020). Greenhouse gas observation network design for Africa. *Tellus B: Chemical and Physical Meteorology*, 72(1), 1824486. https://doi.org/10.1080/16000889.2020.1824486

Nickless, A., Ziehn, T., Rayner, P. J., Scholes, R. J., & Engelbrecht, F. (2015). Greenhouse gas network design using backward Lagrangian particle dispersion modelling—Part 2: Sensitivity analyses and South African test case. *Atmospheric Chemistry and Physics*, 15(4), 2051–2069. https://doi.org/10.5194/acp-15-2051-2015
Niwa, Y., Fujii, Y., Sawa, Y., Iida, Y., Ito, A., Satoh, M., et al. (2017). A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0)—Part 2: Optimization scheme and identical twin experiment of atmospheric CO₂ inversion. *Geoscientific Model Development*, 10(6), 2201–2219. https://doi.org/10.5194/gmd-10-2201-2017

Niwa, Y., Ishijima, K., Ito, A., & Iida, Y. (2022). Toward a long-term atmospheric CO₂ inversion for elucidating natural carbon fluxes: Technical notes of NISMON-CO₂ v2021.1. *Progress in Earth and Planetary Science*, 9, 42. https://doi.org/10.1186/s40645-022-00502-6

Niwa, Y., Tomita, H., Satoh, M., Imasu, R., Sawa, Y., Tsuboi, K., et al. (2017). A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0)—Part 1: Offline forward and adjoint transport models. *Geoscientific Model Development*, 10(3), 1157–1174. https://doi.org/10.5194/gmd-10-1157-2017

Ogle, S. M., Buendia, L., Butterbach-Bahl, K., Breidt, F. J., Hartman, M., Yagi, K., et al. (2013). Advancing national greenhouse gas inventories for agriculture in developing countries: Improving activity data, emission factors and software technology. *Environmental Research Letters*, 8(1), 015030. https://doi.org/10.1088/1748-9326/8/1/015030

Organization of the Petroleum Exporting Countries (OPEC). (2024). Member countries. Retrieved from https://www.opec.org/opec_web/en/about_us/25.htm

Patra, P. K., Dlugokencky, E. J., Elkins, J. W., Dutton, G. S., Tohjima, Y., Sasakawa, M., et al. (2022). Forward and inverse modelling of atmospheric nitrous oxide using MIROC4-Atmospheric Chemistry-Transport Model. *Journal of the Meteorological Society of Japan*, 100(2), 361–386. https://doi.org/10.2151/jmsj.2022-018

Patra, P. K., Houweling, S., Krol, M., Bousquet, P., Belikov, D., Bergmann, D., et al. (2011). TransCom model simulations of CH₄ and related species: Linking transport, surface flux and chemical loss with CH₄ variability in the troposphere and lower stratosphere. *Atmospheric Chemistry and Physics*, 11(24), 12813–12837. https://doi.org/10.5194/acp-11-12813-2011

Patra, P. K., Saeki, T., Dlugokencky, E. J., Ishijima, K., Umezawa, T., Ito, A., et al. (2016). Regional methane emission estimation based on observed atmospheric concentrations (2002–2012). *Journal of the Meteorological Society of Japan*, 94(1), 91–113. https://doi.org/10.2151/jmsj.2016-006

Patra, P. K., Takigawa, M., Watanabe, S., Chandra, N., Ishijima, K., & Yamashita, Y. (2018). Improved chemical tracer simulation by MIROC4.0-based atmospheric chemistry-transport model (MIROC4-ACTM). *SOLA*, 14, 91–96. https://doi.org/10.2151/sola.2018-016

Petersen, S. O., Blanchard, M., Chadwick, D., Del Prado, A., Edouard, N., Mosquera, J., & Sommer, S. G. (2013). Manure management for greenhouse gas mitigation. *Animal*, 7(2s), 266–282. https://doi.org/10.1017/S1751731113000736

Petrescu, A. M. R., Qiu, C., McGrath, M. J., Peylin, P., Peters, G. P., Ciais, P., et al. (2023). The consolidated European synthesis of CH₄ and N₂O emissions for the European Union and United Kingdom: 1990–2019. *Earth System Science Data*, 15(3), 1197–1268. https://doi.org/10.5194/essd-15-1197-2023

Prinn, R., Weiss, R., Arduini, J., Arnold, T., DeWitt, H. L., Fraser, P., et al. (2022). The Advanced Global Atmospheric Gases Experiment (AGAGE) data [Dataset]. Retrieved from https://doi.org/10.15485/1841748

Prinn, R., Weiss, R., Arduini, J., Arnold, T., DeWitt, H. L., Fraser, P. J., et al. (2018). History of chemically and radiatively important atmospheric gases from the Advanced Global Atmospheric Gases Experiment (AGAGE). *Earth System Science Data*, 10(2), 985–1018. https://doi.org/10.5194/essd-10-985-2018

Pyarali, K., Peng, J., Disse, M., & Tuo, Y. (2022). Development and application of high resolution SPEI drought dataset for Central Asia. *Scientific Data*, 9, 172. https://doi.org/10.1038/s41597-022-01279-5

Qin, X., & Tian, H. (2024). Data and scripts for figures for “Greenhouse gas budgets of Central and West Asia (2000–2020): A significant net source to the atmosphere” [Dataset]. *Zenodo*. https://doi.org/10.5281/zenodo.14566089

Randerson, J. T., Chapin, F. S., Harden, J. W., Neff, J. C., & Harmon, M. E. (2002). Net ecosystem production: A comprehensive measure of net carbon accumulation by ecosystems. *Ecological Applications*, 12(4), 937–947. https://doi.org/10.1890/1051-0761(2002)012[0937:NEPACM]2.0.CO;2

R Core Team. (2024). R: A language and environment for statistical computing [Software]. *R Foundation for Statistical Computing*. Retrieved from https://www.R-project.org/

Remaud, M., Chevallier, F., Cozic, A., Lin, X., & Bousquet, P. (2018). On the impact of recent developments of the LMDz atmospheric general circulation model on the simulation of CO₂ transport. *Geoscientific Model Development*, 11(11), 4489–4513. https://doi.org/10.5194/gmd-11-4489-2018

Roby, M. C., Scott, R. L., & Moore, D. J. P. (2020). High vapor pressure deficit decreases the productivity and water use efficiency of rain-induced pulses in semiarid ecosystems. *Journal of Geophysical Research: Biogeosciences*, 125(10), e2020JG005665. https://doi.org/10.1029/2020JG005665

Rodenbeck, C., Houweling, S., Gloor, M., & Heimann, M. (2003). CO₂ flux history 1982–2001 inferred from atmospheric data using a global inversion of atmospheric transport. *Atmospheric Chemistry and Physics*, 3(6), 1919–1964. https://doi.org/10.5194/acp-3-1919-2003

Rodenbeck, C., Zaehle, S., Keeling, R., & Heimann, M. (2018). History of El Niño impacts on the global carbon cycle 1957–2017: A quantification from atmospheric CO₂ data. *Philosophical Transactions of the Royal Society B: Biological Sciences*, 373(1760), 20170303. https://doi.org/10.1098/rstb.2017.0303

Rosentreter, J. A., Laruelle, G. G., Bange, H. W., Bianchi, T. S., Busecke, J. J. M., Cai, W.-J., et al. (2023). Coastal vegetation and estuaries are collectively a greenhouse gas sink. *Nature Climate Change*, 13(6), 579–587. https://doi.org/10.1038/s41558-023-01682-9

Saunois, M., Martinez, A., Poulter, B., Zhen, Z., Raymond, P., Regnier, P., et al. (2024). Global methane budget 2000-2020. *Earth System Science Data*. preprint. https://doi.org/10.5194/essd-2024-115

Schuh, A. E., Jacobson, A. R., Basu, S., Weir, B., Baker, D., Bowman, K., et al. (2019). Quantifying the impact of atmospheric transport uncertainty on CO₂ surface flux estimates. *Global Biogeochemical Cycles*, 33(4), 484–500. https://doi.org/10.1029/2018GB006086

Schuldt, K. N., Aalto, T., Andrews, A., Aoki, S., Arduini, J., Baier, B., et al. (2021). Multi-laboratory compilation of atmospheric methane data for the period 1983-2020. *obspacech_4_1_GLOBALVIEWplus_v4.0_2021-10-14* [Dataset]. *NOAA Global Monitoring Laboratory*. Retrieved from https://doi.org/10.25925/20211001

Schuldt, K. N., Jacobson, A. R., Aalto, T., Andrews, A., Bakwin, P., Bergamaschi, P., et al. (2022). Multi-laboratory compilation of atmospheric carbon dioxide data for the period 2021-2022. *obspace_co2_1_NRT_v7.2_2022-06-28* [Dataset]. *NOAA Earth System Research Laboratory, Global Monitoring Laboratory*. Retrieved from https://doi.org/10.25925/20220624

Schuldt, K. N., Jacobson, A. R., Aalto, T., Andrews, A., Bergamaschi, P., Biermann, T., et al. (2021). Multi-laboratory compilation of atmospheric methane data for the year 2021. *obspace_ch_4_1_NRT_v4.0_2022-03-03* [Dataset]. *NOAA Earth System Research Laboratory, Global Monitoring Laboratory*. Retrieved from https://doi.org/10.25925/20211101

Schuldt, K. N., Mund, J., Luijkx, I. T., Aalto, T., Abshire, J. B., Aikin, K., et al. (2021). Multi-laboratory compilation of atmospheric carbon dioxide data for the period 1957-2020. *obspace_co2_1_GLOBALVIEWplus_v7.0_2021-08-18* [Dataset]. *NOAA Global Monitoring Laboratory*. Retrieved from https://doi.org/10.25925/20210801
Semerjian, H. G., Wong, T. M., & Whetstone, J. R. (2024). NIST greenhouse gas measurements program: A decade of critical accomplishments (No. NIST TN 2291) (p. NIST TN 2291). National Institute of Standards and Technology (U.S.). https://doi.org/10.6028/NIST.TN.2291

Sivrikaya, F., Baskent, E. Z., & Bozali, N. (2013). Spatial dynamics of carbon storage: A case study from Turkey. *Environmental Monitoring and Assessment*, **185**(11), 9403–9412. https://doi.org/10.1007/s10661-013-3260-x

Solazzo, E., Crippa, M., Guizzardi, D., Muntean, M., Choulga, M., & Janssens-Maenhout, G. (2021). Uncertainties in the Emissions Database for Global Atmospheric Research (EDGAR) emission inventory of greenhouse gases. *Atmospheric Chemistry and Physics*, **21**(7), 5655–5683. https://doi.org/10.5194/acp-21-5655-2021

South, A. (2011). rworldmap: A new R package for mapping global data [Software]. *The R Journal*, **3**(1), 35–43. Retrieved from https://doi.org/10.32614/RJ-2011-006

Statista. (2024a). Ammonia production worldwide in 2023, by country. Retrieved from https://www.statista.com/statistics/1266244/global-ammonia-production-by-country/

Statista. (2024b). Production of urea worldwide in 2022, by region. Retrieved from https://www.statista.com/statistics/1287037/global-urea-production-by-region/

Stavert, A. R., Saunois, M., Canadell, J. G., Poulter, B., Jackson, R. B., Regnier, P., et al. (2022). Regional trends and drivers of the global methane budget. *Global Change Biology*, **28**(1), 182–200. https://doi.org/10.1111/gcb.15901

Steele, L. P., Fraser, P. J., Rasmussen, R. A., Khalil, M. A. K., Conway, T. J., Crawford, A. J., et al. (1987). The global distribution of methane in the troposphere. *Journal of Atmospheric Chemistry*, **5**, 125–171. https://doi.org/10.1007/BF00048857

Tang, J., Yin, H., Wang, G., & Chen, Y. (2010). Methane microseepage from different sectors of the Yakela condensed gas field in Tarim Basin, Xinjiang, China. *Applied Geochemistry*, **25**(8), 1257–1264. https://doi.org/10.1016/j.apgeochem.2010.05.011

Thanwerdas, J., Saunois, M., Berchet, A., Pison, I., Vaughn, B. H., Michel, S. E., & Bousquet, P. (2022). Variational inverse modelling within the Community inversion framework to assimilate $\delta^{13}C(CH_4)$ and $CH_4$: A case study with model LMDz-SACS. *Geoscientific Model Development*, **15**(12), 4831–4851. https://doi.org/10.5194/gmd-15-4831-2022

Thompson, R. L., Chevallier, F., Crotwell, A. M., Dutton, G., Langenfelds, R. L., Prinn, R. G., et al. (2014). Nitrous oxide emissions 1999 to 2009 from a global atmospheric inversion. *Atmospheric Chemistry and Physics*, **14**(4), 1801–1817. https://doi.org/10.5194/acp-14-1801-2014

Thorpe, A. K., Green, R. O., Thompson, D. R., Brodrick, P. G., Chapman, J. W., Elder, C. D., et al. (2023). Attribution of individual methane and carbon dioxide emission sources using EMIT observations from space. *Science Advances*, **9**(46), eadh2391. https://doi.org/10.1126/sciadv.adh2391

Tian, H., Bian, Z., Shi, H., Qin, X., Pan, N., Lu, C., et al. (2022). History of anthropogenic nitrogen inputs (HaNi) to the terrestrial biosphere: A 5 arcmin resolution annual dataset from 1860 to 2019. *Earth System Science Data*, **14**(10), 4551–4568. https://doi.org/10.5194/essd-14-4551-2022

Tian, H., Pan, N., Thompson, R. L., Canadell, J. G., Suntharalingam, P., Regnier, P., et al. (2024). Global nitrous oxide budget (1980–2020). *Earth System Science Data*, **16**(6), 2543–2604. https://doi.org/10.5194/essd-16-2543-2024

Tian, H., Xu, R., Canadell, J. G., Thompson, R. L., Winiwarter, W., Suntharalingam, P., et al. (2020). A comprehensive quantification of global nitrous oxide sources and sinks. *Nature*, **586**, 248–256. https://doi.org/10.1038/s41586-020-2780-0

Tian, H., Yang, J., Lu, C., Xu, R., Canadell, J. G., Jackson, R. B., et al. (2018). The global $N_2O$ model intercomparison project. *Bulletin of the American Meteorological Society*, **99**(6), 1231–1251. https://doi.org/10.1175/BAMS-D-17-0212.1

Tibrewal, K., Ciais, P., Saunois, M., Martinez, A., Lin, X., Thanwerdas, J., et al. (2024). Assessment of methane emissions from oil, gas and coal sectors across inventories and atmospheric inversions. *Communications Earth & Environment*, **5**, 26. https://doi.org/10.1038/s43247-023-01190-w

Tsuruta, A., Aalto, T., Backman, L., Hakkarainen, J., Van Der Laan-Luijkx, I. T., Krol, M. C., et al. (2017). Global methane emission estimates for 2000–2012 from CarbonTracker Europe-CH4 v1.0. *Geoscientific Model Development*, **10**(3), 1261–1289. https://doi.org/10.5194/gmd-10-1261-2017

Tubiello, F. N., Karl, K., Flammini, A., Gütschow, J., Obli-Laryea, G., Conchedda, G., et al. (2022). Pre- and post-production processes increasingly dominate greenhouse gas emissions from agri-food systems. *Earth System Science Data*, **14**(4), 1795–1809. https://doi.org/10.5194/essd-14-1795-2022

Tubiello, F. N., Rosenzweig, C., Conchedda, G., Karl, K., Gütschow, J., Xueyao, P., et al. (2021). Greenhouse gas emissions from food systems: Building the evidence base. *Environmental Research Letters*, **16**(6), 065007. https://doi.org/10.1088/1748-9326/ac018e

Umirova, G. K., Istekova, S. A., & Modin, I. N. (2016). Magnetotelluric soundings for estimating the oil- and-gas content of the Mesozoic complex in Western Kazakhstan. *Moscow University Geology Bulletin*, **71**(5), 361–367. https://doi.org/10.3103/S0145875216050094

United Nations. (2024). World population prospects 2024 [Dataset]. United Nations. Retrieved from https://population.un.org/wpp/

United Nations Framework Convention on Climate Change (UNFCCC). (2022). Greenhouse gas inventory data [Dataset]. UNFCCC. Retrieved from https://di.unfccc.int/flex_non_annex1

U.S. Energy Information Administration (EIA). (2024). International data [Dataset]. EIA. Retrieved from https://www.eia.gov/international/data/world

Van Der Laan-Luijkx, I. T., Van Der Velde, I. R., Van Der Veen, E., Tsuruta, A., Stanislawska, K., Babenhauserheide, A., et al. (2017). The CarbonTracker Data Assimilation Shell (CTDAS) v1.0: Implementation and global carbon balance 2001–2015. *Geoscientific Model Development*, **10**(7), 2785–2800. https://doi.org/10.5194/gmd-10-2785-2017

Van Der Werf, G. R., Randerson, J. T., Giglio, L., Van Leeuwen, T. T., Chen, Y., Rogers, B. M., et al. (2017). Global fire emissions estimates during 1997–2016. *Earth System Science Data*, **9**(2), 697–720. https://doi.org/10.5194/essd-9-697-2017

Van Meter, K. J., Van Cappellen, P., & Basu, N. B. (2018). Legacy nitrogen may prevent achievement of water quality goals in the Gulf of Mexico. *Science*, **360**(6387), 427–430. https://doi.org/10.1126/science.aar4462

Wang, C., Wood, J., Wang, Y., Geng, X., & Long, X. (2020). $CO_2$ emission in transportation sector across 51 countries along the Belt and Road from 2000 to 2014. *Journal of Cleaner Production*, **266**, 122000. https://doi.org/10.1016/j.jclepro.2020.122000

Wang, F., Maksyutov, S., Tsuruta, A., Janardanan, R., Ito, A., Sasakawa, M., et al. (2019). Methane emission estimates by the global high-resolution inverse model using national inventories. *Remote Sensing*, **11**(21), 2489. https://doi.org/10.3390/rs11212489

Wang, X., Gao, Y., Jeong, S., Ito, A., Bastos, A., Poulter, B., et al. (2024). The greenhouse gas budget of terrestrial ecosystems in East Asia since 2000. *Global Biogeochemical Cycles*, **38**(2), e2023GB007865. https://doi.org/10.1029/2023GB007865

Wells, K. C., Millet, D. B., Bousserez, N., Henze, D. K., Griffis, T. J., Chaliyakunnel, S., et al. (2018). Top-down constraints on global $N_2O$ emissions at optimal resolution: Application of a new dimension reduction technique. *Atmospheric Chemistry and Physics*, **18**(2), 735–756. https://doi.org/10.5194/acp-18-735-2018

West, T. O., & McBride, A. C. (2005). The contribution of agricultural lime to carbon dioxide emissions in the United States: Dissolution, transport, and net emissions. *Agriculture, Ecosystems & Environment*, **108**(2), 145–154. https://doi.org/10.1016/j.agee.2005.01.002
Wickham, H. (2016). ggplot2: Elegant graphics for data analysis [Software]. Springer-Verlag. Retrieved from https://ggplot2.tidyverse.org

Wilson, C., Chipperfield, M. P., Gloor, M., & Chevallier, F. (2014). Development of a variational flux inversion system (INVICAT v1.0) using the TOMCAT chemical transport model. Geoscientific Model Development, 7(5), 2485-2500. https://doi.org/10.5194/gmd-7-2485-2014

Winiwarter, W., Höglund-Isaksson, L., Klimont, Z., Schöpp, W., & Amann, M. (2018). Technical opportunities to reduce global anthropogenic emissions of nitrous oxide. Environmental Research Letters, 13(1), 014011. https://doi.org/10.1088/1748-9326/aa9ec9

World Bank. (2024). World development indicators [Dataset]. World of Banking. Retrieved from https://databank.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG/1ff4a498/Popular-Indicators#

World Meteorological Organization (WMO). (2019). 20th WMO/IAEA meeting on carbon dioxide, other Greenhouse Gases and Related Measurement Techniques (GGMT-2019) (No. GAW report No. 242). Switzerland. Retrieved from https://library.wmo.int/viewer/57135/download?file=Final_GAW_255.pdf&type=pdf&navigator=1

World Meteorological Organization (WMO). (2024). State of the global climate 2023 (No. WMO-No. 1347). Geneva, Switzerland. Retrieved from https://library.wmo.int/viewer/68835/download?file=1347_Global-statement-2023_en.pdf&type=pdf&navigator=1

Xu, H., Wang, X., & Zhang, X. (2016). Decreased vegetation growth in response to summer drought in Central Asia from 2000 to 2012. International Journal of Applied Earth Observation and Geoinformation, 52, 390-402. https://doi.org/10.1016/j.jag.2016.07.010

Xu, R., Tian, H., Pan, S., Prior, S. A., Feng, Y., & Dangal, S. R. S. (2020). Global N₂O emissions from cropland driven by nitrogen addition and environmental factors: Comparison and uncertainty analysis. Global Biogeochemical Cycles, 34(12), e2020GB006698. https://doi.org/10.1029/2020GB006698

Zaehle, S., Ciais, P., Friend, A. D., & Prieur, V. (2011). Carbon benefits of anthropogenic reactive nitrogen offset by nitrous oxide emissions. Nature Geoscience, 4(9), 601-605. https://doi.org/10.1038/ngeo1207

Zamanian, K., Zarebanadkouki, M., & Kuzyakov, Y. (2018). Nitrogen fertilization raises CO₂ efflux from inorganic carbon: A global assessment. Global Change Biology, 24(7), 2810-2817. https://doi.org/10.1111/gcb.14148

Zamanian, K., Zhou, J., & Kuzyakov, Y. (2021). Soil carbonates: The unaccounted, irrecoverable carbon source. Geoderma, 384, 114817. https://doi.org/10.1016/j.geoderma.2020.114817

Zhang, B., Tian, H., Lu, C., Chen, G., Pan, S., Anderson, C., & Poulter, B. (2017). Methane emissions from global wetlands: An assessment of the uncertainty associated with various wetland extent data sets. Atmospheric Environment, 165, 310-321. https://doi.org/10.1016/j.atmosenv.2017.07.001

Zhang, C., & Ren, W. (2017). Complex climatic and CO₂ controls on net primary productivity of temperate dryland ecosystems over central Asia during 1980-2014. Journal of Geophysical Research: Biogeosciences, 122(9), 2356-2374. https://doi.org/10.1002/2017JG003781

Zhang, Z., Fluet-Chouinard, E., Jensen, K., McDonald, K., Hugelius, G., Gumbricht, T., et al. (2021). Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M). Earth System Science Data, 13(5), 2001-2023. https://doi.org/10.5194/essd-13-2001-2021

Zhang, Z., Poulter, B., Melton, J. R., Riley, W. J., Allen, G. H., Beerling, D. J., et al. (2024). Ensemble estimates of global wetland methane emissions over 2000-2020. Biogeosciences, 22(1), 305-321. https://doi.org/10.5194/bg-22-305-2025

Zhao, C., Liu, B., Wang, J., Xue, R., Shan, Y., Cui, C., et al. (2023). Emission accounting and drivers in Central Asian countries. Environmental Science and Pollution Research, 30, 102894-102909. https://doi.org/10.1007/s11356-023-29608-0

Zhao, Y., Saunois, M., Bousquet, P., Lin, X., Berchet, A., Hegglin, M. I., et al. (2019). Inter-model comparison of global hydroxyl radical (OH) distributions and their impact on atmospheric methane over the 2000-2016 period. Atmospheric Chemistry and Physics, 19(21), 13701-13723. https://doi.org/10.5194/acp-19-13701-2019

Zhou, X., Lv, X., Tao, Y., Wu, L., Havrilla, C. A., & Zhang, Y. (2019). Divergent responses of nitrous oxide, methane and carbon dioxide exchange to pulses of nitrogen addition in a desert in Central Asia. Catena, 173, 29-37. https://doi.org/10.1016/j.catena.2018.09.048

Ziehn, T., Law, R. M., Rayner, P. J., & Roff, G. (2016). Designing optimal greenhouse gas monitoring networks for Australia. Geoscientific Instrumentation Methods and Data Systems, 5(1), 1-15. https://doi.org/10.5194/gi-5-1-2016

Ziehn, T., Nickless, A., Rayner, P. J., Law, R. M., Roff, G., & Fraser, P. (2014). Greenhouse gas network design using backward Lagrangian particle dispersion modelling—Part 1: Methodology and Australian test case. Atmospheric Chemistry and Physics, 14(17), 9363-9378. https://doi.org/10.5194/acp-14-9363-2014

Zittis, G., Almazroui, M., Alpert, P., Ciais, P., Cramer, W., Dahdal, Y., et al. (2022). Climate change and weather extremes in the Eastern Mediterranean and Middle East. Reviews of Geophysics, 60(3), e2021RG000762. https://doi.org/10.1029/2021RG000762

## References From the Supporting Information

Arora, V. K., Melton, J. R., & Plummer, D. (2018). An assessment of natural methane fluxes simulated by the CLASS-CTEM model. Biogeosciences, 15(15), 4683-4709. https://doi.org/10.5194/bg-15-4683-2018

Asaadi, A., & Arora, V. K. (2021). Implementation of nitrogen cycle in the CLASSIC land model. Biogeosciences, 18(2), 669-706. https://doi.org/10.5194/bg-18-669-2021

Bastviken, D., Tranvik, L. J., Downing, J. A., Crill, P. M., & Enrich-Prast, A. (2011). Freshwater methane emissions offset the continental carbon sink. Science, 331(6013), 50. https://doi.org/10.1126/science.1196808

Beerling, D., & Woodward, F. I. (2001). Vegetation and the terrestrial carbon cycle: The first 400 million years. Cambridge University Press.

Deemer, B. R., Harrison, J. A., Li, S. Y., Beaulieu, J. J., Delsontro, T., Barros, N., et al. (2016). Greenhouse gas emissions from reservoir water surfaces: A new global synthesis. BioScience, 66(11), 949-964. https://doi.org/10.1093/biosci/biw117

Delire, C., Séférían, R., Decharme, B., Alkama, R., Calvet, J., Carrer, D., et al. (2020). The global land carbon cycle simulated with ISBA-CTRIP: Improvements over the last decade. Journal of Advances in Modeling Earth Systems, 12(9), e2019MS001886. https://doi.org/10.1029/2019MS001886

Delsontro, T., Beaulieu, J. J., & Downing, J. A. (2018). Greenhouse gas emissions from lakes and impoundments: Upscaling in the face of global change. Limnology and Oceanography Letters, 3(3), 64-75. https://doi.org/10.1002/lol2.10073

Gedney, N., Huntingford, C., Comyn-Platt, E., & Wilshire, A. (2019). Significant feedbacks of wetland methane release on climate change and the causes of their uncertainty. Environmental Research Letters, 14(8), 084027. https://doi.org/10.1088/1748-9326/ab2726

Harrison, J. A., Prairie, Y. T., Mercier-Blais, S., & Soued, C. (2021). Year-2020 global distribution and pathways of reservoir methane and carbon dioxide emissions according to the greenhouse gas from reservoirs (G-res) model. Global Biogeochemical Cycles, 35(6), e2020GB006888. https://doi.org/10.1029/2020GB006888

Haverd, V., Smith, B., Nieradzik, L., Briggs, P. R., Woodgate, W., Trudinger, C. M., et al. (2018). A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation
based approach to plant coordination of photosynthesis. *Geoscientific Model Development*, 11(7), 2995-3026. https://doi.org/10.5194/gmd-11-2995-2018

Holgerson, M. A., & Raymond, P. A. (2016). Large contribution to inland water CO₂ and CH₄ emissions from very small ponds. *Nature Geo-science*, 9(3), 222-226. https://doi.org/10.1038/ngeo2654

Hopcroft, P. O., Valdes, P. J., & Beerling, D. J. (2011). Simulating idealized Dansgaard-Oeschger events and their potential impacts on the global methane cycle. *Quaternary Science Reviews*, 30(23-24), 3258-3268. https://doi.org/10.1016/j.quascirev.2011.08.012

Hopcroft, P. O., Ramstein, G., Pugh, T. A. M., Hunter, S. J., Murguia-Flores, F., Quiquet, A., et al. (2020). Polar amplification of Pliocene climate by elevated trace gas radiative forcing. *Proceedings of the National Academy of Sciences of the United States of America*, 117(38), 23401-23407. https://doi.org/10.1073/pnas.2002320117

Ito, A., & Inatomi, M. (2012). Use of a process-based model for assessing the methane budgets of global terrestrial ecosystems and evaluation of uncertainty. *Biogeosciences*, 9(2), 759-773. https://doi.org/10.5194/bg-9-759-2012

Johnson, M. S., Matthews, E., Bastviken, D., Deemer, B., Du, J., & Genovese, V. (2021). Spatiotemporal methane emission from global Resservoirs. *Journal of Geophysical Research: Biogeosciences*, 126(8), e2021JG006305. https://doi.org/10.1029/2021JG006305

Johnson, M. S., Matthews, E., Du, J., Genovese, V., & Bastviken, D. (2022). Methane emission from global lakes: New spatiotemporal data and observation-driven modeling of methane dynamics indicates lower emissions. *Journal of Geophysical Research: Biogeosciences*, 127(7), e2022JG006793. https://doi.org/10.1029/2022JG006793

Joos, F., Spahni, R., Stocker, B. D., Lienert, S., Müller, J., Fischer, H., et al. (2020). N2O changes from the Last Glacial Maximum to the preindustrial—Part 2: Terrestrial N₂O emissions and carbon-nitrogen cycle interactions. *Biogeosciences*, 17(13), 3511-3543. https://doi.org/10.5194/bg-17-3511-2020

Kato, E., Kinoshita, T., Ito, A., Kawamiya, M., & Yamagata, Y. (2013). Evaluation of spatially explicit emission scenario of land-use change and biomass burning using a process-based biogeochemical model. *Journal of Land Use Science*, 8(1), 104-122. https://doi.org/10.1080/1747423X.2011.628705

Kleinen, T., Brovkin, V., & Schuldt, R. J. (2012). A dynamic model of wetland extent and peat accumulation: Results for the Holocene. *Biogeosciences*, 9(1), 235-248. https://doi.org/10.5194/bg-9-235-2012

Kleinen, T., Mikolajewicz, U., & Brovkin, V. (2020). Terrestrial methane emissions from the Last Glacial Maximum to the preindustrial period. *Climate of the Past*, 16(2), 575-595. https://doi.org/10.5194/cp-16-575-2020

Kleinen, T., Gromov, S., Steil, B., & Brovkin, V. (2021). Atmospheric methane underestimated in future climate projections. *Environmental Research Letters*, 16(9), 119502. https://doi.org/10.1088/1748-9326/ac1814

Kleinen, T., Gromov, S., Steil, B., & Brovkin, V. (2023). Atmospheric methane since the last glacial maximum was driven by wetland sources. *Climate of the Past*, 19(5), 1081-1099. https://doi.org/10.5194/cp-19-1081-2023

Kou-Giesbrecht, S., & Arora, V. K. (2022). Representing the dynamic response of vegetation to nitrogen limitation via biological nitrogen fixation in the CLASSIC land model. *Global Biogeochemical Cycles*, 6(6), e2022GB007341. https://doi.org/10.1029/2022GB007341

Lauerwald, R., Laruelle, G. G., Hartmann, J., Ciais, P., & Regnier, P. A. G. (2015). Spatial patterns in CO₂ evasion from the global river network. *Global Biogeochemical Cycles*, 29(5), 534-554. https://doi.org/10.1002/2014GB004941

Lauerwald, R., Regnier, P., Figueiredo, V., Enrich-Prast, A., Bastviken, D., Lehner, B., et al. (2019). Natural lakes are a minor global source of N₂O to the atmosphere. *Global Biogeochemical Cycles*, 33(12), 1564-1581. https://doi.org/10.1029/2019GB006261

Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., et al. (2019). The Community Land Model version 5: Description of new features, benchmarking, and impact of forcing uncertainty. *Journal of Advances in Modeling Earth Systems*, 11(12), 4245-4287. https://doi.org/10.1029/2018MS001583

Lienert, S., & Joos, F. (2018). A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions. *Bio-geosciences*, 15(9), 2909-2930. https://doi.org/10.5194/bg-15-2909-2018

Liu, S., Kuhn, C., Amatulli, G., Aho, K., Butman, D. E., Allen, G. H., et al. (2022). The importance of hydrology in routing terrestrial carbon to the atmosphere via global streams and rivers. *Proceedings of the National Academy of Sciences of the United States of America*, 119(11), e2106322119. https://doi.org/10.1073/pnas.2106322119

Louis, V. L. S., Kelly, C. A., Duchemin, É., Rudd, J. W. M., & Rosenberg, D. M. (2000). Reservoir surfaces as sources of greenhouse gases to the atmosphere: A global estimate. *BioScience*, 50(9), 766-775. https://doi.org/10.1641/0006-3568(2000)050[0766:RSASOG]2.0:CO₂

Maavara, T., Lauerwald, R., Laruelle, G. G., Akbarzadeh, Z., Bouskill, J., Van Cappellen, P., & Regnier, P. (2019). Nitrous oxide emissions from inland waters: Are IPCC estimates too high? *Global Change Biology*, 25(2), 473-488. https://doi.org/10.1111/gcb.14504

Marzadri, A., Amatulli, G., Tonina, D., Bellin, A., Shen, L. Q., Allen, G. H., & Raymond, P. A. (2021). Global riverine nitrous oxide emissions: The role of small streams and large rivers. *Science of the Total Environment*, 776, 145148. https://doi.org/10.1016/j.scitotenv.2021.145148

Meiyappan, P., Jain, A. K., & House, J. I. (2015). Increased influence of nitrogen limitation on CO₂ emissions from future land use and land use change. *Global Biogeochemical Cycles*, 30(9), 1524-1548. https://doi.org/10.1002/2015GB005086

Melton, J. R., & Arora, V. K. (2016). Competition between plant functional types in the Canadian Terrestrial Ecosystem Model (CTEM) v. 2.0. *Geoscientific Model Development*, 9(1), 323-361. https://doi.org/10.5194/gmd-9-323-2016

Poulter, B., Frank, D. C., Hodson, E. L., & Zimmermann, N. E. (2011). Impacts of land cover and climate data selection on understanding terrestrial carbon dynamics and the CO₂ airborne fraction. *Biogeosciences*, 8(8), 2027-2036. https://doi.org/10.5194/bg-8-2027-2011

Raymond, P. A., Hartmann, J., Lauerwald, R., Sobek, S., McDonald, C., Hoover, M., et al. (2013). Global carbon dioxide emissions from inland waters. *Nature*, 503, 355-359. https://doi.org/10.1038/nature12760

Reick, C. H., Gayler, V., Goll, D., Hagemann, S., Heidkamp, M., Nabel, J. E. M. S., et al. (2021). JSBACH 3—The land component of the MPI Earth system model: Documentation of version 3.2. *Berichte Zur Erdsystemforschung*, 240. https://doi.org/10.17617/2.3279802

Riley, W. J., Subin, Z. M., Lawrence, D. M., Swenson, S. C., Torn, M. S., Meng, L., et al. (2011). Barriers to predicting changes in global terrestrial methane fluxes: Analyses using CLM4Me, a methane biogeochemistry model integrated in CESM. *Biogeosciences*, 8(7), 1925-1953. https://doi.org/10.5194/bg-8-1925-2011

Ringeval, B., Friedlingstein, P., Koven, C., Ciais, P., De Noblet-Ducoudré, N., Decharme, B., & Cadule, P. (2011). Climate-CH₄ feedback from wetlands and its interaction with the climate-CO₂ feedback. *Biogeosciences*, 8(8), 2137-2157. https://doi.org/10.5194/bg-8-2137-2011

Rosentreter, J. A., Borges, A. V., Deemer, B. R., Holgerson, M. A., Liu, S., Song, C., et al. (2021). Half of global methane emissions come from highly variable aquatic ecosystem sources. *Nature Geoscience*, 14(4), 225-230. https://doi.org/10.1038/s41561-021-00715-2

Shu, S., Jain, A. K., Koven, C. D., & Mishra, U. (2020). Estimation of permafrost SOC stock and turnover time using a land surface model with vertical heterogeneity of permafrost soils. *Global Biogeochemical Cycles*, 34(11), e2020GB006585. https://doi.org/10.1029/2020GB006585

Shu, S., Jain, A. K., & Kheshgi, H. S. (2020). Investigating wetland and nonwetland soil methane emissions and sinks across the Contiguous United States using a land surface model. *Global Biogeochemical Cycles*, 34(7), e2019GB006251. https://doi.org/10.1029/2019GB006251