Changes In CO2 Emissions Research Articles

Integrating Process Simulation and Life Cycle Assessment for Enhanced Process Efficiency and Reduced Environmental Impact in Ferromanganese Production

Process simulation was integrated with life cycle assessment to evaluate process efficiency and environmental impact of the production of manganese ferroalloys for various production modes and different ore mineralogies. Utilizing HSC Sim, the model was designed to evaluate the production process with or without a pretreatment step, where results for simulated cases using a four-zone ferromanganese furnace model were exported to openLCA for a complete life cycle assessment. Two main production scenarios were simulated: a closed ferromanganese furnace running on the duplex method and an open furnace running on the discard slag approach. The closed furnace scenario achieved a 17.5% reduction in energy consumption and a 16.2% decrease in direct CO2 emissions with CO-rich off-gas pretreatment. The open furnace scenario showed an 11.2% reduction in energy consumption with thermal solar energy pretreatment but no change in CO2 emissions.

A regression-based approach to the CO2 airborne fraction

The global fraction of anthropogenically emitted carbon dioxide (CO2) that stays in the atmosphere, the CO2 airborne fraction, has been fluctuating around a constant value over the period 1959 to 2022. The consensus estimate of the airborne fraction is around 44%. In this study, we show that the conventional estimator of the airborne fraction, based on a ratio of changes in atmospheric CO2 concentrations and CO2 emissions, suffers from a number of statistical deficiencies. We propose an alternative regression-based estimator of the airborne fraction that does not suffer from these deficiencies. Our empirical analysis leads to an estimate of the airborne fraction over 1959–2022 of 47.0% (± 1.1%; 1σ), implying a higher, and better constrained, estimate than the current consensus. Using climate model output, we show that a regression-based approach provides sensible estimates of the airborne fraction, also in future scenarios where emissions are at or near zero.

A political economy analysis of the income inequality-CO2 emissions nexus in Canada

ABSTRACT Our study investigates the association between income inequality and carbon dioxide (CO2) emissions in Canada, using data from 1981 to 2021. We employ the autoregressive distributed lag bounds testing approach to cointegration. The central finding is a positive long-run cointegrating relationship between changes in the share of income of the top 1% and changes in CO2 emissions. Specifically, a 1% increase in the share of income of the top 1% is associated with a 0.07% increase in CO2 emissions. This finding, which aligns with the political economy argument and the Veblen effect, has significant policy implications. One is that it underscores the importance of considering income redistribution in the context of addressing environmental degradation in Canada.

Trend analysis on CO2 emissions and their implications: a comparative study between India and China.

This research paper aims to provide a comparative trend analysis of CO2 emissions from the two largest emitters, India and China. The analysis focuses on the main sources of CO2 emissions-coal, oil, cement, and gas and their annual data and global share percentages from 1960 to 2019. The study uses non-parametric trend analysis methods, which do not rely on assumptions of normality, outliers, or data length. Pettitt's test, a well-established non-parametric method, was used to detect sudden shifts in the data. The Mann-Kendall (MK) test and Sen's slope estimator were then applied to identify the presence or absence of monotonic linear trends and assess the magnitude of the slopes. In addition, the innovative trend analysis (ITA) method was used, which is particularly effective in detecting and visualizing monotonic, non-monotonic, and sub-trends. The ITA method has the advantage of presenting these trends in a graphical format. According to the results of Pettitt's test, an abrupt change was detected in India in 1989 for all sources of CO2 emissions. In China, however, an abrupt change was detected in 1989 for coal and gas-related sources, while other sources showed a change point in 1990. The results of the MK test and the ITA method showed that all sources show only monotonic increasing trends. Based on the results of Sen's slope estimator, the average rate of change of CO2 emissions is significantly higher in China than in India in all categories after the detection of the abrupt change point. Policymakers should promote the adoption of renewable energy sources, such as solar, wind, hydro, and geothermal, and also implement strategies to control deforestation to counteract the abnormal increase in CO2 emissions. Finally, this research lays a solid foundation for future studies on CO2 emissions.

A plant-based diet is feasible in patients with Crohn's disease

BackgroundIncorporating plant-based diets as a supplement to medical treatment may have a beneficial impact on patients with Crohn's disease, however, research with intervention studies is required. ObjectiveTo investigate the feasibility of a plant-based diet intervention. Secondly, the purpose was to investigate whether such diet may reduce disease activity and enhance quality of life. Materials and methodsThis study was designed as a single arm feasibility study. Outpatients with Crohn’s disease in biological therapy were guided over twelve weeks towards a dietary lifestyle change. Outcome measuresFeasibility concerning recruitment, retention rate and compliance. Secondary outcomes were measures of patient reported outcome questionnaires (PROMS). Paired t-tests were used to examine changes in CO2 emissions, anthropology, biomarkers, and patient-reported data. Δ-values were used to investigate difference between dietary intake and requirements. Linear regression analyses examined the association between biomarkers and PROMS. ResultsIn total, 15 participants completed the intervention with easy recruitment and a retention rate at 87.6%. A clinically positive tendency was seen towards improved symptom scores for disease (HBI; p=0.028 and IBDQ; p=0.006) but not for fatigue (IBD-F; p=0.097), although none of these were statistically significant. Adverse effects were decreased protein intake (p=0.69) and slightly reduced muscle mass. It remains unclear to what extent the intervention contributed to the improved self-reported effects although perception of disease activity was improved. ConclusionThis study demonstrates that it is possible to retain patients following a plant-based diet. However, the dietary change required ongoing dietetic support with a focus on anti-inflammatory agents and the still unattainable protein requirements.

Recent CO2 emission and projections in Chinese provinces: New drivers and ensemble forecasting

Although extensive studies focused on the driver of changing CO2 emission, the roles of labor and capital were largely ignored in shaping spatiotemporal change in CO2 emission and forecasting differences on CO2 emission was few considered, hindering relevant policymaking towards sustainable development in both climate change mitigation and economic growth for developing countries in particular. To fill the gap above, the study explored the roles of capital and labor in contributing to recent CO2 emission in a case of China over 2010–2019 and projecting provincial CO2 emissions to 2030, by proposing two new spatiotemporal logarithmic mean Divisia index models with Cobb-Douglas production function and developing an ensemble forecasting model including machine learning. We found, first, the effects of capital and labor inputs and carbon factor were the positive drivers affecting aggregate CO2 emissions, while the effects of the total-factor productivity and energy intensity were negative drivers. Second, the effects of capital and labor inputs were the negative drivers for narrowing the emission gap. Third, the ensemble forecasting model can improve the generalization ability of CO2 emission predictions. Therefore, we recommend that policymakers focus on optimizing the carbon reduction effects of capital and labor inputs while promoting the development of a circular economy to achieve sustainable economic growth.

Influence of extreme 2022 heatwave on megacities' anthropogenic CO2 emissions in lower-middle reaches of the Yangtze River

An unprecedented heatwave hit the Yangtze River Basin (YRB) in August 2022. We analyzed changes of anthropogenic CO2 emissions in 8 megacities over lower-middle reaches of the YRB, using a near-real-time gridded daily CO2 emissions dataset. We suggest that the predominant sources of CO2 emissions in these 8 megacities are from the power and industrial sectors. In comparison to the average emissions for August in 2020 and 2021, the heatwave event led to a total increase in power sector emissions of approximately 2.70 Mt CO2, potentially due to the increase in urban cooling demand. Suzhou experienced the largest increase, with a rise of 1.12 Mt CO2 (12.88 %). Importantly, we observed that changes in daily power emissions exhibited strong linear relationships with temperatures during the heatwave, albeit varying sensitivities across different megacities (with an average of 0.0076 ± 0.0075 Mt d−1 °C−1). Conversely, we find that industrial emissions decreased by a total of 8.45 Mt CO2, with Shanghai seeing the largest decrease of 4.71 Mt CO2, while Hangzhou experienced the largest relative decrease (−21.22 %). It is noteworthy that the majority of megacities rebounded in industrial emissions following the conclusion of the heatwave. We convincingly suggest a tight linkage between the reductions in industrial emissions and China's policy to ensure household power supply. Overall, the reduction in industrial emissions offset the increase in power sector emissions, resulting in weaker emissions for majority of megacities during the heatwave. Despite remaining uncertainties in the emissions data, our study may offer valuable insights into the complexities of anthropogenic CO2 emissions in megacities amidst frequent summer heatwaves intensified by greenhouse warming.

Modeling carbon dioxide emissions reduction

The paper proposes a new scientific approach to modeling carbon dioxide (CO2) emissions in individual countries, based on the tools of Kohonen self-organizing maps and the provisions of economic theory. This work is based on conducted a comprehensive analysis of wide set of indicators influencing carbon dioxide emissions and decarbonization processes in 40 selected countries for 10 years time span from 2013 till 2022. During clustering these nations using self-organizing maps, 14 key indicators were chosen that cover economic and demographic growth, energy consumption, CO2 emission data, and includes trade of energy recourses for wider analysis and identification of countries with similar decarbonization potential, economic development and trade energy recourses possibilities. This approach exposed distinct clusters of nations with varying decarbonization capabilities and tracked the progress of analyzed countries in terms of CO2 emission changes in dynamic.The analysis of clustering was focused on identification leaders and followers in decarbonization activities. The leaders cluster includes Sweden, Norway, New Zealand and other countries exhibited leadership in developing a climate-resilient economy among 40 countries. Hence, all countries positioned closer to the leader cluster on the map demonstrated higher efficiency in their decarbonization pathways. Consequently, these properties of Kohonen maps provide a foundation for formulating general recommendations to achieve an efficient and effective low-carbon economy for followers including Ukraine, Kazakhstan and other energy intensive countries.Distinct countries decarbonization profiles resulting from clustering were used to develop practical recommendations towards estimating and establishing targeted CO2 emission levels for analyzed countries, conducting continued promoting renewable energy utilization, enhancing cross-border energy trade, diversifying energy portfolios, etc. Also, the proposed methodology can be utilized to project future emissions trends for each country based on cluster-specific models and facilitate policy decisions making process related to mitigating carbon emissions.

Factors driving reduction in CO2 emissions from personal travel: A repeated cross-sectional analysis

This study identifies factors that drive the changes in CO2 emissions from personal travel in urban areas during 2006 and 2016 using repeated cross-sectional household travel surveys conducted in Seoul, Korea. We first estimated the daily travel CO2 emissions of every survey participant for each year based on travel itinerary records and estimated CO2 intensity for the travel mode. The results suggest that total CO2 emissions from personal travel in Seoul declined remarkably between 2006 and 2016, potentially due to reduced vehicle use, transition to non-motorized travel, improved vehicle technology, and eased traffic conditions. Two multilevel mixed-effect Tobit regression models were developed to identify the factors affecting CO2 emissions from personal travel for each year. The results suggest that some cohorts (e.g. males, 30s and 40s, small households, high-income groups, car owners) are likely to produce more CO2 emissions from personal travel consistently in 2006 and 2016. The changes in the estimated coefficients between the two years were also evaluated statistically, suggesting that some population groups (e.g. elderly, large households) are likely to reduce CO2 emissions from personal travel. The reduction in CO2 emissions by these groups may be correlated with continued investment in the public transportation system in Seoul. These findings provide an opportunity to gain a clear understanding of travel behavioral changes related to CO2 emissions from personal travel, with insights toward sustainable development.

The impact of opening the Arctic Northeast Passage on China's carbon emissions

The increase in shipping along Arctic routes has resulted in an increasing need for the environmental effects of shipping to be considered. Existing studies on the environmental impact of Arctic routes have focused mainly on shipping operations and have paid little attention to the dynamic features of opening new shipping routes. In light of the continuing retreat of Arctic sea ice, this study aimed to predict changes in China's CO2 emissions from a production perspective via scenarios of the opening of the Arctic Northeast Passage (NEP) in different future years (2025, 2030, 2035). A shipping cost model was first developed to calculate the changes in shipping costs resulting from commercial use of the NEP. Then, an energy-environmental version of the Global Trade Analysis Project (GTAP-E) model and the GTAP-E 10.0 database were used to simulate the changes in CO2 emissions that may arise from the economic adjustments to the opening of the NEP. The driving factors and possible mechanisms of the environmental effects of the NEP were analyzed based on the simulation results. The re-simulation for the environmental effects of opening the NEP taking emission control policies into account was performed. The findings showed that the opening of the NEP would increase global CO2 emissions. This effect would gradually increase over time. For China, opening the NEP would initially reduce short- and medium-term CO2 emissions owing to upgrades of sectoral structure, while ultimately elevating emissions in the long run due to a large expansion in trade and the economy. The economic growth driven by commercial use of the NEP would tend to optimize the Chinese sectoral and energy structure in the short run. Notably, emission control policies would markedly affect the aforementioned environmental outcomes of opening the NEP.

Prediction of changes in war-induced population and CO2 emissions in Ukraine using social media

Monitoring the changes in population and anthropogenic CO2 emissions caused by geopolitical conflicts is significant for humanitarian assistance and also for revealing the CO2 emission patterns of human activities. However, the changes in population and anthropogenic CO2 emissions are highly dynamic, and representative survey data are generally unavailable. We monitor the near-real-time and fine-grained changes in war-induced population and anthropogenic CO2 emissions in Ukraine through social media data. One year after the invasion, over 11 million Ukrainians are displaced from the baseline 0.1∘*0.1∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${0.1}^{\\circ}\\,*\\,{0.1}^{\\circ}$$\\end{document} gridded regions. There is a significant correlation between the estimated changes and the reference changes for each month in all CO2 emission sectors, with R2 respectively ranging from 0.57 to 0.93, 0.41 to 0.9, 0.74 to 0.99 for residential consumption, ground transport, and industry sectors. Overall, the proposed method provides a new perspective to monitor Ukrainian refugee crisis and measure the spatio-temporal response of anthropogenic CO2 emissions.

Asymmetric impact of energy consumption & financial development on environment using ARDL approach: Case of ASEAN countries

Current research explores the adverse impact of energy consumption and economic development in the ASEAN region. The choice of the region stems from the fact that the human development index of the region ranges widely from 0.93 of Singapore to that of Myanmar, which is 0.58. besides that, the region's Ecological Footprint is increasing with time. Another aspect of the region stems from the fact that, on average, the region has had an ecological deficit since the 1970s. This research examines the origins of environmental degradation in South Asia by analyzing the lopsided impact of urbanization and economic expansion on economic growth and energy use. This study applies the non-linear autoregressive distributive lag (NARDL) method to examine the asymmetry that results from both the positive and negative disruptions of financial development and energy consumption, using an annual dataset spanning 1980 to 2021. According to the NARDL model's findings, both positive and negative shocks in energy consumption significantly contribute to increasing environmental degradation over the long run and decreasing the density of CO2 emissions in the short run. While several factors can cause short-term changes in CO2 emissions, only an adverse shift in financial development can have a lasting and major effect on global emissions. In addition, the ARDL model's findings suggest that less environmental deterioration occurs due to economic progress, but CO2 emissions increase due to rising energy consumption. This article argues that governments should try to achieve high economic success through energy consumption and economic growth that is less taxing on the environment.

Annual and Seasonal Dynamics of CO2 Emissions in Major Cities of China (2019–2022)

To control the growth of CO2 emissions and achieve the goal of carbon peaking, this study carried out a detailed spatio-temporal analysis of carbon emissions in major cities of China on a city-wide and seasonal scale, used carbon emissions as an indicator to explore the impact of COVID-19 on human activities, and thereby studied the urban resilience of different cities. Our research re-vealed that (i) the seasonal patterns of CO2 emissions in major cities of China could be divided into four types: Long High, Summer High, Winter High, and Fluctuations, which was highly related to the power and industrial sectors. (ii) The annual trends, which were strongly affected by the pan-demic, could be divided into four types: Little Impact, First Impact, Second Impact, and Both Impact. (iii) The recovery speed of CO2 emissions reflected urban resilience. Cities with higher levels of de-velopment had a stronger resistance to the pandemic, but a slower recovery speed. Studying the changes in CO2 emissions and their causes can help to make timely policy adjustments during the economic recovery period after the end of the pandemic, provide more references to urban resilience construction, and provide experience for future responses to large-scale emergencies.

Determinants of Yearly CO2 Emission Fluctuations: A Machine Learning Perspective to Unveil Dynamics

In order to understand the dynamics in climate change, inform policy decisions and prompt timely action to mitigate its impact, this study provides a comprehensive analysis of the short-term trend of the year-on-year CO2 emission changes across ten countries, considering a broad range of factors including socioeconomic factors, CO2-related industry, and education. This study uniquely goes beyond the common country-based analysis, offering a broader understanding of the interconnected impact of CO2 emissions across countries. Our preliminary regression analysis, using the ten most significant features, could only explain 66% of the variations in the target. To capture the emissions trend variation, we categorized countries by the change in CO2 emission volatility (high, moderate, low with upward or downward trends), assessed using standard deviation. We employed machine learning techniques, including feature importance analysis, Partial Dependence Plots (PDPs), sensitivity analysis, and Pearson and Canonical correlation analyses, to identify influential factors driving these short-term changes. The Decision Tree Classifier was the most accurate model, with an accuracy of 96%. It revealed population size, CO2 emissions from coal, the three-year average change in CO2 emissions, GDP, CO2 emissions from oil, education level (incomplete primary), and contribution to temperature rise as the most significant predictors, in order of importance. Furthermore, this study estimates the likelihood of a country transitioning to a higher emission category. Our findings provide valuable insights into the temporal dynamics of factors influencing CO2 emissions changes, contributing to the global efforts to address climate change.

Interrelationship between environmental degradation, geopolitical risk, crude oil prices, and green bonds: Evidence from the globe by sectoral analysis

By considering the motion of “the world is our home”, this study investigates the interrelationship between environmental degradation, geopolitical risk, crude oil prices, and green bonds across the globe. So, the research considers the probable differences between the sectors by making a detailed analysis of CO2 emissions across sectors, applies the novel WLMC method, and uses daily data from 2nd January 2020 through 30th November 2023 for a time and frequency-based empirical analysis. The outcomes present that (i) the geopolitical risk is the most important variable on CO2 emissions between 2020/1 and 2021/11 followed by Brent oil prices for the period 2021/12–2023/10; (ii) green bonds are highly effective on CO2 emissions only around 2023/11; (iii) the impacts of the variables are quite weaker (stronger) at lower (higher) frequencies; (iv) the impacts of the variables are the same at aggregated and disaggregated levels; (v) the impact of the variables on CO2 emissions change based on times and frequencies. Thus, the outcomes demonstrate new insights for differentiating impacts across times and frequencies, whereas the direction of the impacts is the same across aggregated and disaggregated levels.

Dynamic effect of disaggregated level electricity generation on residential carbon emissions: Daily inference from the largest EU economies

This study examines the dynamic effects of electricity generation (EG) on CO2 emissions from the residential sector. The study focuses on the EU-4 countries (Germany, Spain, France, and Italy), considers residential CO2 emissions as the dependent variable, and includes disaggregated level fossil and renewable EG as explanatory variables. In this context, the study runs nonlinear quantile-on-quantile (QQ) regression and Granger causality in quantiles (GQ) as the main models with daily data from January 2, 2019, to March 10, 2023, while quantile regression (QR) is used for robustness check. The findings present that in terms of CO2 emissions: (i) EG from coal, natural gas, and oil has a stimulating effect at higher quantiles in all countries; (ii) EG from hydro has an increasing effect at higher quantiles, while it has a decreasing effect at lower and middle quantiles in all countries except France; (iii) EG from solar has a dampening effect at higher quantiles in all countries except France; (iv) EG from wind has a declining effect at higher quantiles in both Spain and France; (v) both fossil and renewable energy EG have a causal effect on residential sector CO2 emissions at the disaggregated level except at some quantiles. Overall, the effect size and the causal effect of EG on CO2 emissions change for quantiles, countries, and EG sources. Therefore, the study proposes to rely on the specific EG sources for Germany and Italy (solar energy), Spain, and France (wind energy) to mitigate climate change by reducing residential CO2 emissions.

Sustained Reductions of Bay Area CO2 Emissions 2018-2022.

Cities represent a significant and growing portion of global carbon dioxide (CO2) emissions. Quantifying urban emissions and trends over time is needed to evaluate the efficacy of policy targeting emission reductions as well as to understand more fundamental questions about the urban biosphere. A number of approaches have been proposed to measure, report, and verify (MRV) changes in urban CO2 emissions. Here we show that a modest capital cost, spatially dense network of sensors, the Berkeley Environmental Air Quality and CO2 Network (BEACO2N), in combination with Bayesian inversions, result in a synthesis of measured CO2 concentrations and meteorology to yield an improved estimate of CO2 emissions and provide a cost-effective and accurate assessment of CO2 emissions trends over time. We describe nearly 5 years of continuous CO2 observations (2018-2022) in a midsized urban region (the San Francisco Bay Area). These observed concentrations constrain a Bayesian inversion that indicates the interannual trend in urban CO2 emissions in the region has been a modest decrease at a rate of 1.8 ± 0.3%/year. We interpret this decrease as primarily due to passenger vehicle electrification, reducing on-road emissions at a rate of 2.6 ± 0.7%/year.

Hydroprocessing of fossil fuel-based aviation kerosene – Technology options and climate impact mitigation potentials

Aviation contributes about 4 % of global anthropogenic climate forcing primarily by contrails, CO2 and NOx emissions. Renewably sourced aviation kerosene can help to reduce the climate impact from CO2 and from contrails, but so far, its production capacities are very small. Hence, the climate impact of using fossil fuel-based kerosene with a hydrogen content increased by hydroprocessing as short term mitigation measure is studied here. Therefore, the change in net energy forcing (ΔEFnet) in 2019 is calculated as the sum of the change in contrail energy forcing (ΔEFcontrail) and additional CO2 emissions (ΔEFhydroprocessing) from aviation kerosene hydroprocessing (ΔEFnet = ΔEFcontrail + ΔEFhydroprocessing). The results show that hydroprocessed aviation kerosene can reduce the net energy forcing EFnet by about 33 % with ΔEFhydroprocessing penalty of 5 %-points. Increasing the hydroprocessing severity increases the relative climate benefit, which is only slightly affected by the emissions factor for hydroprocessing or the choice of the time horizon. Data limitations about fuel composition and its effect on contrails and climate cause considerable uncertainties and the fuel's compliance with specification standards needs consideration. This study on the climate effect of hydroprocessed fossil kerosene can help to assess near-term measures to reduce the climate impact from aviation.

Soil dependence of biochar composts in mitigating greenhouse gas emissions: An overlooked biophysical mechanism

Biochar-manure co-compost (BM) has garnered considerable attention as a promising soil amendment for improving nutrient retention and mitigating the emission of greenhouse gases (GHGs). However, its efficacy often varies largely from one soil to another. By comparing soil CO2 and N2O effluxes, solution chemistry, enzyme activities, and the abundances of N-cycle genes between BM and manure compost (M) across three soils of different texture classes through microcosm incubations, we aimed to develop biophysical mechanics to untangle the soil-dependent efficacy of BM in mediating soil carbon and nitrogen transformation processes. Compared to M addition, BM addition significantly reduced soil CO2 and N2O emissions, but its effectiveness was soil texture-dependent, being strongest for CO2 and N2O in fine-textured clay loam and coarse-textured sand, respectively. Such soil texture-dependent effects of BM versus M were also observed in soil enzyme activities and gene copy numbers of ammonia-oxidizing bacteria and denitrifiers. Our datasets suggest that BM interacted strongly with soil texture to modulate the pore scale-based diffusion of oxygen, thereby creating divergence in the aeration status among soils. Sequentially, soil phenol oxidase activity was greater in BM than in M under more aerobic conditions but no difference between BM and M in oxygen-limited soils. The balance between oxygen depletion due to microbial respiration of organic amendments and oxygen diffusion through soil/biochar pores also shaped the activities of nitrifying and denitrifying prokaryotes differently between coarse- and fine-textured soils. This logic model well-explained the magnitude of change in soil-dependent CO2 and N2O emissions from organic amendments. The knowledge gained in this work will likely have profound practical ramifications for optimizing BM efficacy in mitigating the GHGs emission.

High starch and hemicellulose labile C degradation functional genes increase soil CO2 emissions follow straw return

Straw return is a key method to regulate soil carbon (C) by microbial utilization accumulating and CO2 releasing, which mainly depends on microbial metabolism processes. The present knowledge of the mode and mechanism of soil CO2 emission under straw return is limited. We established four straw return treatments, including straw shallow return (SS, 0–20 cm), straw subsoil burial (SD, 20–40 cm), straw mulching (SM), and control with no straw (NS). Microbial metabolic limitation and the abundance of C degradation functional genes were quantified using enzyme stoichiometry and high-throughput quantitative PCR-based chip technology, respectively. The results revealed that the addition of straw inputs resulted in an increase in soil nutrient contents and soil CO2 emissions. The cumulative CO2 emissions of SS and SM treatments were 21,013.53 and 20,819.26 kg hm−2, respectively, which significantly increased by 4.70 % and 3.73 % compared with SD treatment. Compared with SM and SD treatments, microbial C and P limitations of SS treatment were significantly enhanced, increasing by 1.15 %–1.20 % and 2.73 %–1.21 %, respectively. Straw return increased the absolute abundance of genes engaged in decomposing diverse C source compounds, although with a reduction in microbial C use efficiency. The absolute abundance of amyA, apu, sga, abfA, xylA, and manB labile C degradation genes was higher in SM than in SD treatment, increasing by 1.11 %–25.88 %. On the contrary, the absolute abundance of glx and lig recalcitrant C degradation genes was lower in SM than in SD treatment, reducing by 2.96 %–3.68 %. Straw return directly impacts soil nutrient contents and indirectly influences microbial biomass carbon (MBC), nutrient limitation, and C degradation genes, as revealed by the partial least squares pathway model, ultimately leading to changes in soil CO2 emissions. This finding should contribute to the enhancement of soil C retention and the selection of appropriate methods for straw incorporation in tillage fields.