Emission Projections Research Articles

Contributions of China's terrestrial ecosystem carbon uptakes to offsetting CO2 emissions under different scenarios over 2001–2060

China is committed to achieving carbon neutrality by 2060, which requires that CO2 emissions from fossil fuels and industrial processes (referred to as CO2 emissions) be offset by carbon uptake through its terrestrial ecosystems. This study quantified the contributions of China's terrestrial carbon sinks to offsetting CO2 emissions over 2001–2060 by combining vegetation model simulations and multiple datasets of CO2 emission projections across different future emission scenarios. Our results showed that China's CO2 emissions will increase from 0.99 Pg C yr−1 in 2001 to >4 Pg C yr−1 in 2060 under the SSP3–7.0 and SSP5–8.5 scenarios; however, for SSP1–2.6, CO2 emissions will eventually decrease to 0.50 Pg C yr−1. Conversely, China's net terrestrial carbon sink was projected to increase from −0.14 Pg C yr−1 (2000s) to [−0.35, −0.40] Pg C yr−1 (2050s) under different SSP scenarios based on vegetation modelling. As a result, China's net terrestrial carbon sink will contribute only approximately 10% to offsetting its CO2 emissions under scenarios SSP3–7.0 and SSP5–8.5, implying an almost certain failure of the carbon-neutral strategy. In contrast, under scenario SSP1–2.6, approximately 50%–80% of CO2 emissions can be offset by the terrestrial carbon sink by 2060, in line with the possible success of the carbon-neutral goal. This study underscores the critical role of terrestrial carbon sink in achieving carbon neutrality in China and hightlights the remaining challenges for further reducing CO2 emissions.

Agricultural Carbon Emissions in China: Estimation, Influencing Factors, and Projection of Peak Emissions

Agricultural Carbon Emissions in China: Estimation, Influencing Factors, and Projection of Peak Emissions

Methane emissions from the natural gas industry in China – A systematically accounting based on the bottom-up approach

The comprehensive and meticulous assessment of methane (CH4) emissions stemming from the natural gas industry stands as an imperative endeavor in steering the natural gas industry toward a trajectory of low-carbon sustainability. This study has meticulously constructed detailed facility-level inventories for CH4 emissions within China's natural gas industry and used a bottom-up approach to fully account for both direct and indirect CH4 emissions. The findings reveal a notable increase in CH4 emissions within the natural gas industry in China, rising from 2.04 Mt in 2012 to 3.94 Mt in 2021. Regionally, Shaanxi, Sichuan, and Xinjiang emerged as the primary emission contributors in 2020, signifying a shift from northeastern and northern China to northwestern and southwestern regions during the 2012–2020 period. The fluctuations in emissions, both overall and regionally, closely align with the evolving landscape of China's natural gas sector. Projections for future CH4 emissions indicate a continued upward trajectory in tandem with industry growth. Consequently, the study emphasizes the urgency of implementing emission reduction measures in China's natural gas industry. Timely intervention is crucial to ensuring the orderly progression of the industry while effectively curbing the associated CH4 emissions.

Enhancing Transparency of Climate Efforts: MITICA’s Integrated Approach to Greenhouse Gas Mitigation

Under the Paris Agreement, countries must articulate their most ambitious mitigation targets in their Nationally Determined Contributions (NDCs) every five years and regularly submit interconnected information on greenhouse gas (GHG) aspects, including national GHG inventories, NDC progress tracking, mitigation policies and measures (PAMs), and GHG projections in various mitigation scenarios. Research highlights significant gaps in the definition of mitigation targets and the reporting on GHG-related elements, such as inconsistencies between national GHG inventories, projections, and mitigation targets, a disconnect between PAMs and mitigation scenarios, as well as varied methodological approaches across sectors. To address these challenges, the Mitigation-Inventory Tool for Integrated Climate Action (MITICA) provides a methodological framework that links national GHG inventories, PAMs and GHG projections, applying a hybrid decomposition approach that integrates machine learning regression techniques with classical forecasting methods for developing GHG emission projections. MITICA enables mitigation scenario generation until 2050, incorporating over 60 PAMs across Intergovernmental Panel on Climate Change (IPCC) sectors. It is the first modelling approach that ensures consistency between reporting elements, aligning NDC progress tracking and target setting with IPCC best practices while linking climate change with sustainable economic development. MITICA’s results include projections that align with observed trends, validated through cross-validation against test data, and employ robust methods for evaluating PAMs, thereby establishing its reliability.

Interpretable short-term carbon dioxide emissions forecasting based on flexible two-stage decomposition and temporal fusion transformers

Carbon emissions play a pivotal role in exacerbating the global warming crisis and driving climate change. Accurate and consistent projections of carbon emissions are of utmost importance for nations worldwide, as they shape emission reduction strategies and expedite the pursuit of carbon peaking and carbon neutrality goals. While previous studies have focused on hybrid methodologies for carbon emission forecasting, yielding commendable predictive performance, these approaches often overlook the significance of internal interpretability within the forecasting models. In light of this gap, this study introduces a groundbreaking and elucidating hybrid carbon emission forecasting model that amalgamates the two-stage layer decomposition method, adaptive differential evolution with optional external archive (JADE), and temporal fusion transformers (TFT). To begin with, a series of sub-sequences is derived by employing a flexible two-stage decomposition strategy, which leverages a linear-nonlinear decomposition criterion to thoroughly extract the fluctuating characteristics inherent in the carbon emission series. Subsequently, the JADE algorithm intelligently and efficiently optimizes the parameter combinations within the TFT model, ensuring both stability and reliability of the prediction framework. Empirical investigations conclusively demonstrate the remarkable applicability and efficacy of the proposed model in short-term carbon emission forecasting. By delving into the interpretability of the model's results, the study enhances the capacity of policymakers to devise well-informed strategies based on comprehensive insights gleaned from the forecasting process.

Modeling the Impact of Climate Change on Streamflow in the Meghna River Basin: An Analysis Using SWAT and CMIP6 Scenarios

This study assesses future climate change impacts on the hydrologic response of the Upper Meghna River Basin (UMRB), a major river system in Bangladesh. Separate SWAT (Soil and Water Assessment Tool) hydrologic models were developed for the three major sub-basins of the UMRB, i.e., Barak, Meghalaya, and Tripura, considering their unique geographical, hydrological, and land-use characteristics. To evaluate the efficiency of multi-site modeling in providing better model performance, the SWAT models were calibrated at both single and multiple locations. Those models were then simulated to estimate future flows using climate projection data from thirteen CMIP6 General Circulation Models (GCMs) under moderate and extreme emission scenarios, SSP2-4.5, and SSP5-8.5. The results revealed that the annual maximum flow will keep increasing gradually with time. The outlets of the Meghalaya sub-basin will experience a more significant rise in future flow in the upcoming decades compared to the Barak and Tripura sub-basins. Results showed that dry season flows with increases of up to 31–50% would be less affected compared to the wet periods, which could experience increases of up to 47–66%) across the sub-basins by the end of the 21st century under extreme emission projections. Besides an increasing trend in the mean flow, future flows at several outlets also exhibited an escalation in the intensity and frequency of extreme flood events.

Inventory, Dynamic Evolution, and Scenario Projections of Agricultural Carbon Emissions in Shandong Province, China

The reduction in agricultural carbon emissions (ACEs) in Shandong Province is essential to China’s carbon peak and carbon neutrality objectives. In this regard, we constructed an ACE inventory for Shandong Province at a resolution of 1 km × 1 km, integrating the emission factor method with geographic information system (GIS) technology. Building upon this, we explored the dynamic evolution patterns of ACEs using kernel density estimation and conditional probability density estimation. Additionally, long short-term memory networks were trained to predict ACEs under various scenarios. The results showed that: (1) ACEs in Shandong Province exhibited two stages of change, i.e., “rise and decline”. Notably, 64.39% of emissions originated from the planting industry. The distribution of emissions was closely correlated with regional agricultural production modes. Specifically, CO2 emissions were predominantly distributed in crop cultivation areas, while CH4 and N2O emissions were primarily distributed in livestock breeding areas. The uncertainty of the emission inventory ranged from −12.04% to 10.74%, mainly caused by emission factors. (2) The ACE intensity of various cities in Shandong Province is decreasing, indicating a decoupling between ACEs and agricultural economic growth. Furthermore, the emission disparities among different cities are diminishing, although significant spatial non-equilibrium still persists. (3) From 2022 to 2030, the ACEs in Shandong Province will show a continuous downward trend. By 2030, the projected values under the baseline scenario, low-carbon scenario I, and low-carbon scenario II will be 6301.74 × 104 tons, 5980.67 × 104 tons, and 5850.56 × 104 tons. The low-carbon scenario reveals greater potential for ACE reduction while achieving efficient rural economic development and urbanization simultaneously. This study not only advances the methodology of the ACE inventory but also provides quantitative references and scientific bases for promoting low-carbon, efficient, and sustainable regional agriculture.

Projecting Global Mercury Emissions and Deposition Under the Shared Socioeconomic Pathways

AbstractMercury (Hg) is a naturally occurring element that has been greatly enriched in the environment by human activities like mining and fossil fuel combustion. Despite commonalities in some carbon dioxide (CO2) and Hg emission sources, the implications of long‐range climate scenarios for anthropogenic Hg emissions have yet to be explored. Here, we present comprehensive projections of anthropogenic Hg emissions extending to the year 2300 and evaluate impacts on global atmospheric Hg deposition. Projections are based on four Shared Socioeconomic Pathways (SSPs) ranging from sustainable reductions in resource and energy intensity to rapid economic growth driven by abundant fossil fuel exploitation. There is a greater than two‐fold difference in cumulative anthropogenic Hg emissions between the lower‐bound (110 Gg) and upper‐bound (235 Gg) scenarios. Hg releases to land and water are approximately six times those of direct emissions to air (600–1,470 Gg). At their peak, anthropogenic Hg emissions reach 2,200–2,600 Mg a−1 sometime between 2010 (baseline) and 2030, depending on the SSP scenario. Coal combustion is the largest determinant of differences in Hg emissions among scenarios. Decoupling of Hg and CO2 emission sources occurs under low‐to mid‐range scenarios, though contributions from artisanal and small‐scale gold mining remain uncertain. Future Hg emissions may have lower gaseous elemental Hg (Hg0) and higher divalent Hg (HgII), resulting in a higher fraction of locally sourced Hg deposition. Projected reemissions of previously deposited anthropogenic Hg follow a similar temporal trajectory to primary emissions, amplifying the benefits of primary Hg emission reductions under the most stringent mitigation scenarios.

Estimate Gaps of Montreal Protocol-Regulated Potent Greenhouse Gas HFC-152a Emissions in China Have Been Explained.

1,1-Difluoroethane (HFC-152a) is a hydrofluorocarbon regulated by the Montreal Protocol, and its emissions in China are of concern as China will regulate HFC-152a in 2024. However, no observation-inferred top-down estimates were undertaken after 2017, and substantial gaps existed among previous estimates of China's HFC-152a emissions. Using the atmospheric observations and inverse modeling, this study reveals China's HFC-152a emissions of 9.4 ± 1.7 Gg/yr (gigagrams per year), 10.6 ± 1.8 Gg/yr, and 9.7 ± 1.5 Gg/yr in 2018, 2019, and 2020, respectively. In addition, we display an overall increasing trend during 2011-2020, which is in contrast to the decreasing and steady trend reported by the Emission Database for Global Atmospheric Research (EDGAR) and the Chinese government, respectively. Subsequently, we establish a comprehensive bottom-up emission inventory matching with top-down estimates and thus succeed in explaining the gaps among previous estimates. Furthermore, the contribution of China's emissions to global HFC-152a emission growth increased from 15% during 2001-2010 to >100% during 2011-2020. An emission projection based on our improved inventory shows that the Kigali Amendment (KA) would assist in avoiding 1535.6-4710.6 Gg (251.8-772.5 Tg CO2-eq) HFC-152a emissions during 2024-2100. Our findings indicate relatively accurate China's HFC-152a emissions and provide scientific support for addressing climate change and implementing the KA.

Projections of private vehicle ownership, energy demand and vehicular emissions – a study of metropolitan cities in India

Urban agglomerations in India have witnessed significant growth in private vehicle ownership since the turn of this century. Intense urban road transport will have stern implications on air quality, GHG emissions, energy demands and, transport infrastructure. This study investigates the relationship between income and vehicle ownership rate for five metropolitan cities in India using a non-linear Gompertz functional framework. Medium-term scenarios of private vehicle (cars and two-wheelers) ownership, vehicle stock, energy demand and tailpipe emissions under multiple scenarios are envisaged by deriving ownership saturation based on city area and land use pattern. The results show a significant increase in vehicle stock resulting in a substantial rise in energy demand and vehicular emissions by 2035. However, the adoption of electric vehicles is expected to truncate this surge. Delhi is projected to saturate during this period while other cities are expected to saturate after 2035. The study also highlights transport policy and investment spheres to reduce private vehicle dependency.

A unified modelling framework for projecting sectoral greenhouse gas emissions

Effectively tackling climate change requires sound knowledge about greenhouse gas emissions and their sources. Currently, there is a lack of comprehensive, sectorally disaggregated, yet comparable projections for greenhouse gas emissions. Here, we project sectoral emissions until 2050 under a business-as-usual scenario for a global sample of countries and five main sectors, using a unified framework and Bayesian methods. We show that, without concerted policy efforts, global emissions increase strongly, and highlight a number of important differences across countries and sectors. Increases in emerging economies are driven by strong output and population growth, with emissions related to the energy sector accounting for most of the projected change. Advanced economies are expected to reduce emissions over the coming decades, although transport emissions often still show upward trends. We compare our results to emission projections published by selected national authorities as well as results from Integrated Assessment Models and highlight some important discrepancies.

Trends and driving forces of agricultural carbon emissions: A case study of Anhui, China.

To facilitate accurate prediction and empirical research on regional agricultural carbon emissions, this paper uses the LLE-PSO-XGBoost carbon emission model, which combines the Local Linear Embedding (LLE), Particle Swarm Algorithm (PSO) and Extreme Gradient Boosting Algorithm (XGBoost), to forecast regional agricultural carbon emissions in Anhui Province under different scenarios. The results show that the regional agricultural carbon emissions in Anhui Province generally show an upward and then downward trend during 2000-2021, and the regional agricultural carbon emissions in Anhui Province in 2030 are expected to fluctuate between 11,342,100 tones and 14,445,700 tones under five different set scenarios. The projections of regional agricultural carbon emissions can play an important role in supporting the development of local regional agriculture, helping to guide the input and policy guidance of local rural low-carbon agriculture and promoting the development of rural areas towards a resource-saving and environment-friendly society.

Medium and Long-term Carbon Emission Projections and Emission Reduction Potential Analysis of the Lingang Special Area Based on the LEAP Model

Based on the existing statistical data of the Lingang Special Area in Shanghai and considering its future socio-economic development， industrial structure， and technological development， a LEAP-Lingang model was developed to analyze the evolution trends of energy demand and carbon emissions under the baseline scenario， low-carbon scenario， and enhanced low-carbon scenario. To enhance the prediction accuracy of the model， the Logistic population growth model was used to predict future population data， and the learning curve model was used to simulate the cost evolution trend of related carbon reduction technologies. In addition， an economic evaluation model for carbon reduction technologies was developed， and the economic costs and emission reduction potential of typical carbon reduction technologies were evaluated by drawing a marginal emission reduction cost curve. The results showed that under the enhanced low-carbon scenario， the renewable energy accounted for 69% of the primary energy consumption， and the electric energy accounted for 91% of the terminal energy demand in 2060. The Lingang Special Area could achieve carbon peak by 2030， and the carbon emissions in 2060 were predicted to decrease by 94% compared to that in the baseline scenario. In terms of contribution to emission reduction， clean energy substitution， industrial structure optimization， and terminal energy efficiency improvement played a key role in reducing carbon emissions near the port. In the medium term （until 2035）， they were predicted to contribute 35.1%， 27.3%， and 16.2% of carbon emissions， respectively， and in the long term （until 2060）， they should contribute 50.6%， 8.75%， and 7.7% of carbon emissions， respectively. Regarding specific carbon reduction technologies， hydrogen power generation； water electrolysis for hydrogen； and carbon capture， utilization， and storage （CCUS） technology were of great significance for achieving net-zero emissions， but the costs of emission reduction were relatively high. The research results can provide ideas and references for the low-carbon and green development of the Lingang Special Area and related areas.

Medium-term projections of vehicle ownership, energy demand and vehicular emissions from private road transport in India

Medium-term projections of vehicle ownership, energy demand and vehicular emissions from private road transport in India

Salinity causes widespread restriction of methane emissions from small inland waters

Inland waters are one of the largest natural sources of methane (CH4), a potent greenhouse gas, but emissions models and estimates were developed for solute-poor ecosystems and may not apply to salt-rich inland waters. Here we combine field surveys and eddy covariance measurements to show that salinity constrains microbial CH4 cycling through complex mechanisms, restricting aquatic emissions from one of the largest global hardwater regions (the Canadian Prairies). Existing models overestimated CH4 emissions from ponds and wetlands by up to several orders of magnitude, with discrepancies linked to salinity. While not significant for rivers and larger lakes, salinity interacted with organic matter availability to shape CH4 patterns in small lentic habitats. We estimate that excluding salinity leads to overestimation of emissions from small Canadian Prairie waterbodies by at least 81% ( ~ 1 Tg yr−1 CO2 equivalent), a quantity comparable to other major national emissions sources. Our findings are consistent with patterns in other hardwater landscapes, likely leading to an overestimation of global lentic CH4 emissions. Widespread salinization of inland waters may impact CH4 cycling and should be considered in future projections of aquatic emissions.

The global emission mitigation potential of avoiding waste and product lifespan extension by Chinese households

This paper examines the emission mitigation potential of Chinese households’ low-carbon behavior by 2030 through a global carbon footprint scenario analysis. The emission reduction effect is estimated by comparing the projected global emissions in 2030 in a lifestyle emulation scenario and a low-carbon scenario, in which Chinese households adopt low-carbon consumption behaviors. Lifestyle emulation is modeled based on what we call “world Engel curves”, which describe how the expenditure share of a certain consumption good depends on the total per capita expenditures for household consumption (which depends on income). By including a dynamic link between household lifestyle changes and GDP, we then obtain the emission projections under different scenarios in 2030, based on the historical data for 49 countries from 1995 to 2011 from EXIOBASE. Our results show that adopting a mild low-carbon lifestyle by households helps only little in terms of reducing GHG emissions. Reducing avoidable waste and expanding the lifetime of products are not enough to help meeting the 2 °C goal. More drastic changes are required.

Uncertain multi-period project adjustment and selection under the carbon tax and carbon quota policies

To achieve the carbon neutrality goal, enterprises should consider not only the development of new low-carbon emission projects but also the adjustment of the existing high-carbon emission projects. This paper discusses a multi-period project adjustment and selection (MPPAS) problem under the carbon tax and carbon quota policies. First, we propose an uncertain mean-chance MPPAS model for maximizing the profit of the project portfolio under the carbon tax and carbon quota policies. Then, we provide the deterministic equivalent of the proposed model and conduct the theoretical analysis of the impact of carbon tax and carbon quota policies. Next, we propose an improved adaptive genetic algorithm to solve the proposed model. Finally, we give numerical experiments to verify the proposed algorithm’s performance and show the proposed model’s applicability. Research has shown that the government can achieve the carbon neutrality goal by determining reasonable carbon tax and carbon quota policies, and companies can make the optimal investment decisions for the project portfolio by the proposed model. In addition, the proposed algorithm has good performances in robustness, convergence speed, and global convergence.

Anticipated sulfur dioxide emissions from coal-fired power plants

Southeastern Europe (SEE), which includes Serbia, along with Germany and Poland, is one of the largest producers of solid fuels in Europe. Despite growing environmental concerns, coal, especially lignite, persists and remains the dominant fuel for electricity generation in this region. Concurrently, these power plants emerge as the predominant emitters of pollutants in all the observed states. To conduct a comprehensive analysis, data on major emitters within the European Union were gathered, allowing for a detailed comparison with SEE countries. A specific focus was placed on the thermal power plant Kostolac in Serbia, where the calculation of SO2 emissions for two thermoblocks was undertaken. Comparisons were drawn with emitters in EU countries, with a particular emphasis on SEE nations. The analysis involved data from wells that have undergone excavation over specific periods, facilitating an exploration of the correlation between calculated values, those from exploited wells, and projections of future SO2 emissions from new wells in areas earmarked for surface mining exploitation.

Carbon and Greenhouse Gas Emissions from Electricity Consumption in European Union Buildings

The buildings sector is the single most important end-user of final energy in the European Union and a significant contributor to carbon and greenhouse gas emissions. This work focuses on a review of available data that are used to calculate the annual emissions from electricity generation in the European Union and quantify indirect emissions from the use of electricity in the buildings sector. Historical data since 1990 are used to derive simple empirical correlations for the time evolution of emissions factors related to electricity production in each Member State of the European Union. More recent trajectories using data from the last ten years are also presented. The derived correlations can be easily integrated in building stock modeling and national studies to facilitate forward-looking projections of emissions from electricity use in buildings. The EU-27 averages 0.2883 kgCO2-eq/kWhel, ranging from 0.0456 kgCO2-eq/kWhel in Sweden up to 1.0595 kgCO2-eq/kWhel in Poland. As a case study, the derived coefficients are then used to quantify the indirect emissions from the electricity consumption attributed to the building stock in each EU Member State. The calculated total EU-27 GHG indirect emissions attributed to electricity consumption amounted to 215 MtCO2-eq for residential buildings and 201 MtCO2-eq for non-residential buildings. In addition, the proposed correlations are used to demonstrate how they can be used for more realistic future projections of emissions towards the national targets in Greece and Poland.

Projecting future carbon emissions from cement production in developing countries

Achieving low-carbon development of the cement industry in the developing countries is fundamental to global emissions abatement, considering the local construction industry’s rapid growth. However, there is currently a lack of systematic and accurate accounting and projection of cement emissions in developing countries, which are characterized with lower basic economic country condition. Here, we provide bottom-up quantifications of emissions from global cement production and reveal a regional shift in the main contributors to global cement CO2 emissions. The study further explores cement emissions over 2020-2050 that correspond to different housing and infrastructure conditions and emissions mitigation options for all developing countries except China. We find that cement emissions in developing countries except China will reach 1.4-3.8 Gt in 2050 (depending on different industrialization trajectories), compared to their annual emissions of 0.7 Gt in 2018. The optimal combination of low-carbon measures could contribute to reducing annual emissions by around 65% in 2050 and cumulative emissions by around 48% over 2020-2050. The efficient technological paths towards a low carbon future of cement industry vary among the countries and infrastructure scenarios. Our results are essential to understanding future emissions patterns of the cement industry in the developing countries and can inform policies in the cement sector that contribute to meeting the climate targets set out in the Paris Agreement.