Emission Trends Research Articles

CO2 Emissions Estimate From Mexico City Using Ground‐ and Space‐Based Remote Sensing

AbstractThe Mexico City Metropolitan Area (MCMA) stands as one of the most densely populated urban regions globally. To quantify the urban emissions in the MCMA, we independently assimilated observations from a dense column‐integrated Fourier transform infrared (FTIR) network and OCO‐3 Snapshot Area Map observations between October 2020 and May 2021. Applying a computationally efficient analytical Bayesian inversion technique, we inverted for surface fluxes at high spatio‐temporal resolutions (1‐km and 1‐hr). The fossil fuel (FF) emission estimates of 5.08 and 6.77 Gg/hr reported by the global and local emission inventories were optimized to 4.85 and 5.51 Gg/hr based on FTIR observations over this 7 month period, highlighting a convergence of posterior estimates. The modeled biogenic flux estimate of −0.14 Gg/hr was improved to −0.33 to −0.27 Gg/hr, respectively. It is worth noting that utilizing observations from three primary sites significantly enhanced the accuracy of estimates (13.6 29.2%) around the other four. Using FTIR posterior estimates can improve simulation with the OCO‐3 data set. OCO‐3 shows a similar decreasing trend in FF emissions (from 6.37 Gg/hr to 6.36 and 5.04 Gg/hr) as FTIR, but its correction trends for biogenic sources differ, changing from 0.37 to 0.48 Gg/hr. The primary reason is OCO‐3's lower temporal sampling density. Aligning the FTIR inversion timing with that of OCO‐3 yielded comparable corrections for FF emissions, yet discrepancies in biogenic emissions persisted, which can be attributed to their different sampling locations in the rural region and discrepancy in X observations. Our findings mark a significant step toward validating OCO‐3 and FTIR inversion results in metropolitan region.

Accounting Method and Indicators of Multilevel CO2 Emissions Based on Cost During Construction of Shield Tunnels

With the rapid expansion of shield tunnel projects in China, precise accounting of CO2 emissions throughout the entire process is essential for advancing green and low-carbon construction practices. This paper introduces an innovative CO2 accounting methodology utilizing a cost-based carbon emission coding system. It adopts a multilevel approach to carbon emission accounting, aligned with this coding system, which facilitates a detailed examination of carbon emission ratios and characteristics across various construction techniques. The analysis includes six typical shield tunnel projects varying in diameter, focusing on sub-projects to scrutinize CO2 emissions and establish specific indicators. The findings indicate that CO2 emissions per 10,000 yuan of investment, approximately 30.25% (or 3025 kgCO2e/10,000 yuan), are more consistent than those per unit of length. Moreover, the study highlights differing CO2 emission trends among sub-projects compared to whole tunnel projects, assessing emission indicators and distribution patterns in four sub-projects: shield excavation, segmental lining, internal structure, and tunnel reinforcement. From these findings, the paper suggests more precise and tailored strategies for CO2 reduction. This research provides a theoretical basis for future construction planning and carbon management strategies.

The Moderating Effect of Population Growth on the Relationship between Carbon Emission and Economic Development in Surigao Del Norte, Philippines using Predictive Algorithm

This paper explored the moderating effect of population growth on the relationship between carbon emissions and economic development in Surigao del Norte. The descriptive research design of the study has been complemented by data mining techniques in the analysis of the gathered data. The results revealed that the trend of population growth, economic growth, and carbon emission in Surigao del Norte from 2019-2022 were fluctuating based on pre- and post-pandemic effects as well as increased economic activities, industrial growth, and changes in energy consumption patterns. Moreover, the forecasted trend of population growth, economic development, and carbon emission in the next decade is also fluctuating based on the data but notable increase in the population growth for the province will significantly be seen and since the forecasted trend of population growth, economic growth, and carbon emission is fluctuating, it does have a significant contribution to the Philippines population growth, economic development, and carbon emission in the next decade. Consequently, the economic development in the province of Surigao del Norte poses a significant impact towards the province’s carbon emission in the next years and lastly the population growth of Surigao del Norte has a significant and positive influence towards the economic development of the province and its corresponding carbon emissions in the next years.

Barriers to implement energy efficiency strategies to heating, ventilation, air conditioning of airside system in commercial buildings in Sri Lanka

At present, debates on climate change, fossil fuel depletion, and energy saving highlight the need for a sustainable built environment to reduce energy consumption and emission trends. The Heating, Ventilation, and Air Conditioning (HVAC) system is a priority due to its significant share of a building’s electricity consumption. While energy efficiency strategies for central air conditioning systems focus on both waterside and airside components, implementing airside efficiency strategies is notably lacking in Sri Lanka. This is a concern because airside inefficiencies lead to excessive energy use, higher operational costs, and compromised indoor air quality. Thus, the study aims to address this gap by identifying the barriers to implementing energy efficiency strategies for HVAC airside systems in Sri Lankan commercial buildings. To explore these barriers, a qualitative research approach was employed, involving interviews with 17 industry experts. The study identified 27 barriers, with key barriers being a lack of technical expertise, rapid technological advancements, misalignment with organisational objectives, limited financial resources, inadequate training and technical knowledge, and insufficient regulatory support. The insights gained from this study can assist industry professionals in enhancing HVAC energy performance and encourage further academic research on this sub-branch of HVAC systems in the Sri Lankan context.

Prediksi Risiko Emisi Karbon Dioksida Melalui Pemodelan GSTAR Kriging di Wilayah Asia

This study analyzes the variability of carbon dioxide emissions across Asia, revealing that China, India, and Indonesia are the primary contributors with extremely high average emissions and standard deviations. The GSTAR(3;1,1,1) model has been shown to be optimal for forecasting future emissions, based on lower RMSE and MAPE values. Ordinary Kriging analysis using the isotropic spherical semivariogram model provides the most accurate predictions for unobserved areas, with contour maps indicating that the northeastern region of Asia will continue to face high emission concentrations until 2027. While countries such as Brunei and Armenia have managed to keep emission levels low, the instability of emission trends across Asia underscores the need for emission reduction strategies tailored to the specific context of each country.

Forecasting Carbon Dioxide Emission Regional Difference in China by Damping Fractional Grey Model

The emission of carbon dioxide is the main reason for many global warming problems. Although China has made tremendous efforts to reduce carbon emission, the space–time dynamics of the carbon emission trend is still imbalanced. To forecast CDED in China, the Dagum Gini coefficient was applied to measure regional CDED. Then, a grey correlation model was used to select potential influence factors and a wrapping method for selecting the optimal subset. DGMC is proposed to forecast CDED. The research results showed that the DGMC generalization performance is significantly superior to other models. The MAPE of DGMC in six cases are 1.18%, 1.11%, 0.66%, 1.13%, 1.27% and 0.51%, respectively. The RMSPEPR of DGMC in six cases are 1.08%, 1.21%, 0.97%, 1.36%, 1.41% and 0.57%, respectively. The RMSPEPO of DGMC in six cases are 1.29%, 0.69%, 0.02%, 0.58%, 0.78% and 0.32%, respectively. In future trends, the eastern carbon dioxide emission intraregional differences will decrease. Additionally, the intraregional differences in western and middle-region carbon dioxide emissions will expand. Interregional carbon emission difference will display a narrowing trend. Compared with the traditional grey model and ANN model, integrating the influence factor information significantly improved forecasting accuracy. The proposed model will present better balanced historical information and accurately forecast future trends. Finally, policy recommendations are proposed based on the research results.

Computational study on the impact of gasoline-ethanol blending on autoignition and soot/NOx emissions under low-load gasoline compression ignition conditions

In the present work, computational fluid dynamics (CFD) simulations of a single-cylinder gasoline compression ignition (GCI) engine are performed to investigate the impact of gasoline-ethanol blending on autoignition, nitrogen oxide (NOx), and soot emissions under low-load conditions. In order to represent the test gasoline (RD5-87), a four-component toluene primary reference fuel (TPRF)+ethanol (ETPRF) surrogate (with 10% ethanol by volume; E10) is employed. A three-dimensional (3D) engine CFD model employing finite-rate chemistry with a skeletal kinetic mechanism (including NOx sub-mechanism), adaptive mesh refinement (AMR), and hybrid method of moments (HMOM) is adopted to capture the in-cylinder combustion phenomena and soot/NOx emissions. The engine CFD model is validated against experimental data for three gasoline-ethanol blends: E10, E30 and E100, with varying ethanol content by volume. Model validation is carried out for a broad range of start-of-injection (SOI) timings (−21, −27, −36, and −45 crank angle degrees (°CA) after top-dead-center (aTDC)) with respect to in-cylinder pressure, heat release rate, combustion phasing, NOx and soot emissions. For relatively later injection timings (−21 and −27 °CA aTDC), E30 yields higher amount of soot than E10; while the trend reverses for early injection cases (−36 and −45 °CA aTDC ). On the other hand, E100 yields the lowest amount of soot among all fuels irrespective of SOI timing. Further, E10 shows a non-monotonic trend in soot emissions with SOI timing: SOI-36>SOI-45>SOI-21>SOI-27, while soot emissions from E30 exhibit monotonic decrease with advancing SOI timing. NOx emissions from various fuels follow a trend of E10>E30>E100. On the other hand, NOx emissions increase as SOI timing is advanced for all fuels, with an anomaly for E10 and E100 where NOx decreases when SOI is advanced beyond −36 °CA aTDC. Detailed analysis of the numerical results is performed to investigate the soot/NOx emission trends and elucidate the impact of chemical composition and physical properties on autoignition and emissions characteristics.

Impacts of On‐Road Vehicular Emissions on U.S. Air Quality: A Comparison of Two Mobile Emission Models (MOVES and FIVE)

AbstractOn‐road vehicles are significant contributors to air pollution globally, particularly to nitrogen oxides (NOx) and ozone (O3). Quantifying their contribution to air quality is crucial to understanding the trends of vehicle emissions as low‐ and “zero” emission vehicles join the fleet. Modeling on‐road emissions is complex due to various factors like fleet activities, traffic patterns, and meteorological conditions. We compare on‐road emissions from two mobile models: the National Oceanic and Atmospheric Administration Fuel‐based Inventory of Vehicle Emission (FIVE) and the US Environmental Protection Agency Motor Vehicle Emission Simulator (MOVES), finding they contribute 4%–33% to volatile organic compounds (VOCs), NOx, and fine particulate matter (PM2.5) in the contiguous United States (CONUS). Using a regional chemical transport model, we assess air quality effects under different emission scenarios. Both emission data sets yield satisfactory performance, with MOVES showing lower biases in ozone (O3) and PM2.5 over CONUS, while FIVE performs better at city scales due to higher urban NOx emissions. In January, on‐road emissions increased surface O3 over western and southern US by up to 9.1%–13.1% but decreased by 2.5% over the northeastern US, while PM2.5 predictions vary across the US (−85% to 24%). In July, on‐road emissions elevate O3 and PM2.5 concentrations by 15%–20% across CONUS, except in some west coast cities. They also greatly contribute to nitrogen dioxide (NO2) by more than 80% near roads and in urban areas. This study highlights the significant impact of on‐road emissions on urban air quality and provides insights for improving air quality forecasting and management.

Numerical study on ammonia injection location of ammonia and low-rank coal cofiring in 1000 MWe ultra super critical carolina-type boiler

Global CO2 emissions from coal-fired power plants necessitate innovative solutions to reduce their environmental footprint. Ammonia cofiring has emerged as a promising solution for reducing CO2 emissions in these plants. The ammonia injection optimization is analyzed using computational fluid dynamics simulations in a 1000 MW Ultra Super Critical Coal Fired Power Plant, comparing four injection sites at a 20 % thermal energy cofiring ratio. Results indicated the bottom burner as the most effective location, achieving the lowest NOx emissions (102.20 mg/Nm3) compared to traditional coal-firing (131.78 mg/Nm3). A sensitivity analysis favored bottom and mid burner injections over equal distribution, leading to further investigations into varying injection locations and cofiring ratios (5%–60 %). Increased cofiring ratios decreased gross power output by 3.6 MW per percentage increase in cofiring, alongside shifts in flue gas properties and a complex NOx emission trend. Initially, NOx emissions decreased, with a 0.784 up to 20 % cofiring, but dramatically increased beyond, reaching a ratio of 3.744 at 60 % cofiring. This study reveals critical insights into emission management and highlights the need for improved derating strategies to mitigate acid corrosion risks, marking a significant step towards environmentally sustainable coal power generation.

Numerical Investigations on Reducing Unburned Hydrocarbon and Carbon Monoxide Emissions in Reactivity-Controlled Compression Ignition Using Partial Reactivity Stratification with Alternative Fuels and Additive

<div>A numerical investigation has been performed in the current work on reactivity-controlled compression ignition (RCCI), a low-temperature combustion (LTC) strategy that is beneficial for achieving lower oxides of nitrogen (NO<sub>x</sub>) and soot emission. A light-duty diesel engine was modified to run in RCCI mode. Experimental data were acquired using diesel as HRF (high-reactivity fuel) and gasoline as LRF (low reactivity fuel) to check the accuracy and fidelity of predicted results. Blends of ethanol and gasoline with DTBP (di-tert-butyl peroxide) addition in a small fraction on an energy basis were used in numerical simulations to promote ignitability and reactivity enhancement of PFI charge. Achieving stable, smooth, and gradual combustion in RCCI is challenging at low loads, especially in light-duty engines, due to misfiring and poor combustion stability. DTBP is known for enhancing cetane number and accelerating combustion, and it is mixed in a PFI blend to avoid combustion deterioration. The factors governing reactivity stratification to achieve optimal combustion phasing were investigated in the present study. DTBP decomposition and its low-temperature oxidation chemistry were found to be responsible for affecting combustion phasing, heat release patterns, and emission trends. DTBP additive and different in-cylinder strategies were applied and studied to reduce unburned emissions. Adopting a multiple injection approach utilizing dual-pulse assisted in reducing HC and CO levels. It enhances combustion quality by providing adequate control over combustion phasing. Altering operating parameters like intake temperatures reduced HC, CO, and soot emissions by 97.6%, 57.6%, and 52.8%, respectively, compared to baseline gasoline/diesel RCCI data. Optimizing the injection timings of the first and second pulse helps achieve optimal combustion phasing and a 72.95% reduction in NO<sub>x</sub> emissions. The higher injection pressure of DI helped lower the CO and soot emissions by 53.33% and 51.84%, respectively.</div>

A 40-Year Time Series of Land Surface Emissivity Derived from AVHRR Sensors: A Fennoscandian Perspective

Accurate land surface temperature (LST) retrieval depends on precise knowledge of the land surface emissivity (LSE). Neglecting or inaccurately estimating the emissivity introduces substantial errors and uncertainty in LST measurements. The emissivity, which varies across different surfaces and land uses, reflects material composition and surface roughness. Satellite data offer a robust means to determine LSE at large scales. This study utilises the Normalised Difference Vegetation Index Threshold Method (NDVITHM) to produce a novel emissivity dataset spanning the last 40 years, specifically tailored for the Fennoscandian region, including Norway, Sweden, and Finland. Leveraging the long and continuous data series from the Advanced Very High Resolution Radiometer (AVHRR) sensors aboard the NOAA and MetOp satellites, an emissivity dataset is generated for 1981–2022. This dataset incorporates snow-cover information, enabling the creation of annual emissivity time series that account for winter conditions. LSE time series were extracted for six 15 × 15 km study sites and compared against the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD11A2 LSE product. The intercomparison reveals that, while both datasets generally align, significant seasonal differences exist. These disparities are attributable to differences in spectral response functions and temporal resolutions, as well as the method considering fixed values employed to calculate the emissivity. This study presents, for the first time, a 40-year time series of the emissivity for AVHRR channels 4 and 5 in Fennoscandia, highlighting the seasonal variability, land-cover influences, and wavelength-dependent emissivity differences. This dataset provides a valuable resource for future research on long-term land surface temperature and emissivity (LST&E) trends, as well as land-cover changes in the region, particularly with the use of Sentinel-3 data and upcoming missions such as EUMETSAT’s MetOp Second Generation, scheduled for launch in 2025.

Spatial-temporal dynamics and influencing factors of city level carbon emission of mainland China

Urban areas are major sources of carbon emissions, making it crucial to understand their emission characteristics for effective carbon reduction and sustainable development. Using carbon emission data from mainland China (2001–2021), we analyzed the spatio-temporal dynamics and future trends of city-level emissions and explored influencing factors using machine learning methods. Results indicate significant fluctuations in carbon emissions, with over 40 % of mainland Chinese cities experiencing a doubling in total emissions. Geospatially, cities in South Coast (SC), East Coast (EC), Northeast (NE), and NorthCoast (NC) show stronger intensity and increasing trends in carbon emissions compared to other regions, with over 80 % of cities in these regions experiencing high or higher increases. Additionally, a continued rise in carbon emissions was detected in most Chinese cities, with an average Hurst index of 0.64, indicating persistent trends. Using the XGBoost method, factors such as population density, built-up area, urban green coverage rate, and GDP were found to strongly correlate with urban carbon emissions, exhibiting significant spatial heterogeneity. This research uncovers the characteristics and influencing factors of urban-scale carbon emissions, offering valuable insights for policymakers to tailor carbon reduction strategies to the specific needs and conditions of various urban areas.

An exceptional phytoplankton bloom in the southeast Madagascar Sea driven by African dust deposition

Abstract Rising surface temperatures are projected to cause more frequent and intense droughts in the world's drylands. This can lead to land degradation, mobilization of soil particles, and an increase in dust aerosol emissions from arid and semi-arid regions. Dust aerosols are a key source of bio-essential nutrients, can be transported in the atmosphere over large distances, and ultimately deposited onto the ocean's surface, alleviating nutrient limitation and increasing oceanic primary productivity. Currently, the linkages between desertification, dust emissions and ocean fertilization remain poorly understood. Here, we show that dust emitted from Southern Africa was transported and deposited into the nutrient-limited surface waters southeast of Madagascar, which stimulated the strongest phytoplankton bloom of the last two decades during a period of the year when blooms are not expected. The conditions required for triggering blooms of this magnitude are anomalous, but current trends in air temperatures, aridity, and dust emissions in Southern Africa suggest that such events could become more probable in the future. Together with the recent findings on ocean fertilization by drought-induced megafires in Australia, our results point toward a potential link between global warming, drought, aerosol emissions, and ocean blooms.

Inverse modelling approach to assess air pollutant emission trends, and source contributions in highly polluted cities

Pollutant concentrations are regularly measured in many cities, but emission loads often remain unknown and are irregularly estimated. This paper presents a methodology to estimate total emissions in a highly polluted megacity using an inverse modeling approach. An inverted Fixed Box Model (FBM) has been used with pollutant concentrations and meteorological variables to estimate emissions of pollutants by discounting the meteorological variability from the measured concentrations. Model-predicted emissions are validated with existing inventories and then used to assess annual, monthly, and diurnal trends in emissions. The applicability of the model is demonstrated for Delhi. Trend analysis shows that pollutants are emitted in a unique pattern that also differs over time periods of consideration. Yearly trends in Delhi show that PM10, NOX, and SO2 emissions have increased by 33%, 4%, and 16%, respectively, during 2008–2018. However, due to controls, the emission intensities have been found to stabilize for PM10 and decrease for NOX and SO2 pollutants in Delhi. Monthly trends indicate that summer months have higher emission rates owing to intensive energy demands (in automobiles, power plants), dust storms, and increased re-suspension of dust due to higher wind speeds. However, due to dispersive meteorology, ambient concentrations are lower in summers. The inverse modeling shows that PM10 emissions are dominated by dust sources (73%), while PM2.5 has the largest contribution from transport sector emissions (34%). NOX and SO2 emissions in the city are contributed heavily by the transport (84%) and industrial (92%) sectors, respectively. The model helps in understanding temporal emission trends and source contributions, which can be useful to implement effective control measures and track the progress of control.

Temporal scaling of carbon emission accumulations and rates of the Meso-Cenozoic hyperthermal events: implication to the Anthropocene global warming

Anthropocene global warming is largely associated with fossil fuel carbon emissions. Temporal scaling provides a way to place current carbon emissions on a geological scale. The scaling of carbon emissions at the onset of hyperthermal events suggests that we might anticipate higher carbon emission rates over longer time scales than what we currently observe in the Anthropocene. However, this inference is uncertain due to limited data concerning the accumulations and time intervals of carbon emissions of Meso-Cenozoic hyperthermal events. While on the long-time hyperthermal-event scales of several to hundreds of kiloyears, modern carbon accumulations and emission rates are 9 times greater than those of the hyperthermal-event emissions. The present-day carbon release can be effectively compared to the onset of hyperthermal events through temporal scaling. If current carbon emission trends persist, we may reach the carbon emission thresholds for hyperthermal events in one to three hundred years, getting an intensified hydrological cycle, enhanced continental weathering and ocean acidification. And if the situation gets worse, we may reach the upper limit of the carbon emission threshold for hyperthermal events (e.g., Permian-Triassic Boundary event, PTB) with a biotic mass extinction over four to thirteen hundred years. This study offers new insights into current carbon emissions from a temporal scale perspective, enhancing our understanding of contemporary climate change.

Time Series Forecasting of Emission Trends Using Recurrent Neural Networks

This paper explores the application of Recurrent Neural Networks (RNNs) for forecasting emission trends, a critical aspect of addressing climate change and formulating effective environmental policies. Traditional forecasting methods, such as Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing, often fail to capture the complex nonlinear relationships and temporal dependencies inherent in emission data. In contrast, RNNs, particularly Long Short-Term Memory (LSTM) networks, are designed to recognize patterns in sequential data, making them well-suited for time series forecasting tasks. This study employs a comprehensive methodology that includes data collection from reputable sources, feature selection, RNN model design, and rigorous evaluation using various performance metrics. The results indicate that RNNs significantly outperform traditional forecasting techniques in terms of accuracy, providing valuable insights into future emission trajectories. The findings underscore the potential of RNNs as powerful tools for policymakers and researchers, facilitating more informed decision-making in the fight against climate change.

How can Transportation Sector Achieve High-quality Carbon Peaking in China?

Promoting the peak of carbon emissions in the transportation sector as soon as possible is the key to advancing carbon peaking process in China. This article combines the KAYA model, peak quality standards based on Tapio decoupling model, and international experience to predict the future trend of carbon emissions of transportation sector, and to analyze the path for the transportation sector to achieve high-quality peak in China. Research has found that: (1) Based on China's goals (by 2030, the proportion of oil and gas will decrease to 75%), it has been found that China's transportation sector may achieve carbon peak by 2032, with lower peak quality than developed countries such as Japan. (2) Based on international experience, stable GDP growth and technological progress are key to ensuring the quality of carbon peak in the transportation sector. (3) On the basis of reducing the share of oil and gas to 75% by 2030, if China further decreases the energy intensity of its transportation sector by 27% compared to 2018 levels (a 7% greater decrease than the target scenario), it is projected that China's transportation sector may achieve a high-quality carbon peak by 2030 and potentially match the peak quality level of France. This paper can provide theoretical support for promoting carbon emissions reduction and facilitating the peak in China's transportation sector.

Potentials of greenhouse gas emission reduction through energy efficiency improvement in Iran's petrochemical sector

Reducing energy consumption and increasing energy efficiency is a key strategy to mitigate greenhouse gases (GHGs) in the petrochemical sector, both because of the high share of energy-related emissions and because of the economic attractiveness in terms of return on investment. This paper aims to calculate the GHG mitigation potentials of the energy reduction projects in Iran's petrochemical sector. firstly, we estimated the trend of GHG emissions until 2035 using data collected from the petrochemical complexes in Iran. Then, by assessing the energy audits and other studies, we calculated the energy consumption reduction potentials as well as their costs and benefits for petrochemical complexes. Finally, we modelled the energy supply and demand of the petrochemical sector in LEAP software to evaluate the mitigation potentials of the energy reduction projects. The results show that a 6.7 % emission reduction in the existing petrochemical complexes can be achieved with a capital of about 160 million dollars. The net present value of the projects will be about 2 billion dollars, and the cost of carbon reduction will be −60.7 dollars per ton. The results of this research can be used for policymakers to plan the mitigation pathway of the sector since the revenues obtained from the energy conservation measures can be used to fund capital-intensive GHG mitigation projects.

Evolution of agricultural carbon emission research in China: a historical analysis and emerging trends

Agricultural activities constitute the second-largest contributor to greenhouse gas emissions. Proactively mitigating agricultural carbon emissions is crucial for safeguarding the ecological en-vironment, improving agricultural productivity, and fostering long-term ecological sustainability. This paper employs bibliometric analysis to examine the research status, hot topics, and devel-opment trends of agricultural carbon emissions in China over the past 2 decades. Based on Citespace software, the study primarily conducts visual analysis on 660 academic articles on ag-ricultural carbon emissions collected from the China National Knowledge Infrastructure (CNKI) between 2001 and May 2023, including publications indexed in Peking University Chinese Core Journals (PKU Core), Chinese Social Sciences Citation Index and Chinese Science Citation Database. The analysis covers publication quantity, author cooperation, institution cooperation, keyword co-occurrence, keyword clustering, keyword burst, keyword timeline, and keyword timezone. Research results indicate: (1) From the annual publication volume changes perspective, research on China’s agricultural carbon emissions demonstrates a rapid upward trend in the new era, with increasing research interest. (2) The core net-work of research authors has been established, primarily concentrated in agricultural and forestry universities, and the core network of institutions in this field is gradually forming. However, collaboration networks between authors and research institutions are relatively dispersed, necessitating strengthened collaboration among institutions. (3) Current research on agricultural carbon emissions predominantly focuses on the challenges of reducing agricultural carbon emissions in China under the “dual carbon” goals, measures, and pathways to achieve agricultural carbon emission reductions; performance evaluation of agricultural carbon emissions, factors affecting these emissions, and their reduction potential; as well as the relationship between agricultural carbon emissions and agricultural economic growth. Future research should delve deeper into the precise accounting of agricultural carbon emissions under the “dual carbon” goals, their underlying mechanisms, and issues related to precise and differentiated agricultural carbon reduction strategies. (4) The development trajectory of domestic agricultural carbon emissions research shows a period of germination from 2001 to 2009, a development stage from 2010 to 2015, and a deepening stage from 2016 to 2023, with a notable increase in publications in 2021, signifying a new upward phase in research output.

Carbon emission measurement and regional decomposition analysis of China’s beef cattle farming industry

IntroductionWarming caused by greenhouse gas (GHG) emissions has become a global environmental issue of widespread concern, and China, as a responsible power, has the pressing task of reducing carbon emissions. China is one of the world’s major beef producers and consumers, and at the same time, beef cattle, as a large livestock, is the largest source of GHG emissions in the livestock industry.MethodsThis study considered the panel data of 31 provinces in China from 2008 to 2022. The kernel density estimation and Dagum Gini coefficient were used to analyze the spatiotemporal dynamic evolution patterns and influencing factors of carbon emissions from China’s beef cattle farming industry.Results(1) The carbon emission trajectory of beef cattle production follows a distinctive “ascend-descend-ascend” three-phase pattern. By 2022, the sector’s cumulative carbon emissions had burgeoned by 37.62% relative to 2008, reflecting an average annual escalation of 2.31%. Despite the overall upward trend in carbon emissions, significant regional differences were observed. The Central Plains region has witnessed a consistent decline, in stark contrast to the Southwest and Northeast regions, which have emerged as hotspots for heightened carbon emissions and intensified emission densities within China’s beef cattle production landscape, underscoring the intensifying significance of carbon mitigation measures. (2) The kernel density curve shows an overall rightward shift with a specific gradient effect on carbon emissions. In addition, the range of the right drag of the curve in 2022 was significantly reduced, which laterally reflects the narrowing of the difference between the provinces with the highest and lowest carbon emissions from beef cattle farming. The principal source of variance in the overall carbon emissions from beef cattle production is the disparities between regions, which accounts for an average annual contribution rate of 52.52%. Conversely, the within-region contribution rates have remained relatively stable, while those for the intensity of transvariation have witnessed a substantial rise, with annual averages of 18.31 and 28.96%, respectively. (3) Regarding the factors influencing carbon emissions reduction, environmental regulations and production efficiency significantly drive carbon emissions reduction in beef cattle farming.DiscussionRelevant government departments should actively guide farmers toward green production, establish perfect policies and regulations for low-carbon beef cattle farming, and promote low-carbon beef cattle farming models based on local conditions.