Factors Of Emissions Research Articles

LMDI and STIRPAT Analysis of Carbon Emissions in the Chengdu-Chongqing Region: An Industrial Advancement Perspective

This study investigates the driving factors of carbon emissions in the Chengdu-Chongqing urban agglomeration, a key economic region in western China, using the LMDI decomposition method and the STIRPAT model. The results reveal that energy structure optimization and energy consumption intensity reduction are the primary drivers of carbon emission reductions, highlighting the importance of clean energy adoption and improvements in energy efficiency. In contrast, economic growth and secondary industry expansion are the dominant contributors to carbon emission increases, reflecting the energy-intensive nature of industrial activities and the strong coupling between economic development and energy consumption. The tertiary industry exhibits a dual role, with its expansion reducing emissions in regions with higher levels of green transformation but increasing emissions in areas dominated by traditional service sectors. Industrial advancement (or industrial structural upgrading) emerges as a critical strategy for mitigating emissions, while population size has a relatively small direct impact, though urbanization amplifies emissions in high-density areas. This study contributes methodologically by integrating the LMDI and STIRPAT approaches, providing a robust framework for analyzing carbon emission drivers. Empirically, it highlights significant regional heterogeneity in emission drivers across counties and districts, offering valuable insights for targeted low-carbon development strategies. The findings underscore the need to accelerate clean energy adoption, enhance energy efficiency, promote green industrial transformation, and optimize urbanization patterns. These results provide a scientific basis for formulating policies to achieve carbon neutrality and sustainable development in the Chengdu-Chongqing urban agglomeration.

Impact Factors and Structural Pathways of Carbon Emissions in the Power Sector of the Beijing–Tianjin–Hebei Region Using MRIO Analysis

The accelerated growth of the global economy has given rise to a multitude of environmental concerns that demand immediate attention. At this juncture, the total global carbon emissions are exhibiting a gradual increase. China, the United States, India, Russia, and Japan represent the top five countries in terms of global carbon emissions, collectively accounting for approximately 60% of the global total. Of these, China’s carbon emissions are the highest in the world, representing over 30% of the global total. As urbanization accelerates, the carbon emissions from urban agglomerations constitute a substantial share of the nation’s total emissions, rendering the carbon emissions of urban clusters a critical issue. In the context of China’s urban agglomerations, the Beijing–Tianjin–Hebei region, due to factors such as industrial structure, accounts for a relatively high proportion of carbon emissions, approximately 11% of the national total. The future trajectory of carbon emissions in the Beijing–Tianjin–Hebei region will significantly impact the high-quality development of the entire urban cluster. Consequently, research on carbon emissions in the Beijing–Tianjin–Hebei region is of vital importance. This paper takes the carbon emissions of the power industry in the Beijing–Tianjin–Hebei region as the research subject, analyzes its carbon emissions status, and builds a multi-regional input–output model for the Beijing–Tianjin–Hebei region based on the input–output tables and carbon emissions data of each province. This study explores the key influencing factors of carbon emissions from the power industry in this region from 2012 to 2017 and analyzes the carbon emissions transfer and structural evolution from the perspective of the region and the industry to clarify the carbon reduction responsibilities of the Beijing–Tianjin–Hebei region and provide references and recommendations for the formulation of regional collaborative emission reduction policies. The results show that the direct carbon emissions from the power industry in the Beijing–Tianjin–Hebei region account for a higher proportion compared to the indirect carbon emissions it generates by driving other industries. Industries with relatively high indirect carbon emissions in the key path include coal mining and selection, equipment manufacturing, transportation, services, etc. The capital input process from Tianjin and Hebei to Beijing is accompanied by a relatively high carbon transfer. Promoting the widespread adoption of carbon emission reduction technologies will have an effective suppressive effect on carbon emissions in the Beijing–Tianjin–Hebei region, especially in Hebei; Beijing and Tianjin should pay attention to the stimulating effect of increased final demand on carbon emissions; the transfer of carbon emissions between regions and industries shows a downward trend as the power sector undergoes transformation.

Advancing Sustainability in Surface Coal Mines Through Real-Time Air Quality Monitoring: Low-Cost IoT Solutions and the Role of Meteorological Factors in PM and GHG Emissions

This paper presents a development of a “smart center” for studying particulate matter (PM) and other gaseous emissions pollution in a surface coal mine located in the Jharia coalfield of Jharkhand, India, utilizing internet of things (IOT)-based capabilities and low-cost sensors data. These pollutants in the air in and around surface mines present significant environmental challenges that require comprehensive assessment and management. This study undertook four winter months from January to April 2023 of real-time, low-cost IoT-based monitoring of PM1, PM2.5, PM10, SO2, NO2, O3, CO, CO2, TVOC, and noise pollution, along with meteorological parameters, such as humidity, rainfall, and temperature. Data were collected at three locations: within a mine site area, in a buffer zone located 1.5 km from the mine, and at a non-impacted residential area within the university campus. Mining operations and active mine fires are primary contributors to pollution. Peak PM concentrations around the mining site were observed during morning and evening hours (5–7 a.m. and 4–6 p.m.). This study further identified that air pollutant concentrations were positively correlated with humidity but negatively correlated with the atmospheric temperature at the mine site, but not at the university campus of the IIT–ISM.

Uncovering the multiple socio-economic driving factors of carbon emissions in nine urban agglomerations of China based on machine learning

Uncovering the multiple socio-economic driving factors of carbon emissions in nine urban agglomerations of China based on machine learning

Regional disparities in PPCPs contamination of urban wastewater treatment plants: Unveiling influential factors and ecological effects.

This study investigates the discharge of pharmaceuticals and personal care products (PPCPs) from wastewater treatment plants (WWTPs) into natural waters, highlighting a significant correlation with regional human activities. Despite the complexity of assessing factors influencing PPCPs sources, it remains underexplored. By conducting an extensive literature review of seven categories of PPCPs in WWTPs across five typical regions of China, the study reveals both similarities and disparities in PPCPs composition. Correlation analysis and four machine learning algorithms are employed to identify affecting factors for PPCPs emissions. The findings reveal that regional differences in per capita load are affected by treatment scale and population served by WWTPs. Economic indicators, wastewater treatment efficiency, and population age structure correlate with specific PPCPs. The RF algorithms demonstrated reliable predictive capabilities for PPCPs concentrations, identifying significant influencing factors such as service population, treatment capacity, and economic development. Ecological risk assessments indicate that certain PPCPs, such as norfloxacin (NOR) and ofloxacin (OFL), pose high risks to algae. These findings underscore the necessity for region-specific strategies to address PPCPs challenges, considering factors like economic development, urbanization, and demographic characteristics, and provide valuable insights into PPCPs presence and ecological risks.

Greenhouse gas emissions and mitigation strategies in China's municipal solid waste sector under the impact of the COVID-19 pandemic

With the continuous increase in municipal solid waste (MSW) generation, greenhouse gas (GHG) emissions caused by MSW treatment have gradually become an important environmental concern. This study developed a comprehensive ten-factor analysis framework using the generalized Divisia index method (GDIM) to decompose the driving factors of GHG emissions from MSW sectors in China and its 31 provinces from 2010 to 2022, including population, economy, consumption, and waste generation. The heterogeneity of factor contributions was discussed from both temporal and spatial perspectives to propose mitigation strategies for provincial MSW sectors. The results indicated the following: (1) GHG emissions from the MSW sector increased from 2010 to 2019, followed by a 20.7% decrease from 2019 to 2022 due to the COVID-19 pandemic. Regionally, emissions were greater in the eastern provinces than in the central and western provinces. (2) The main positive contributors to the increase in GHG emissions were the consumption expenditure of urban households (HCE, 27.61 MtCO2e) and GDP (25.71 MtCO2e). GHG emissions per HCE (-27.31 MtCO2e) and GHG emissions per GDP (-25.45 MtCO2e) were the main factors for emission reduction. (3) The contributions of ten driving factors to each province varied significantly across different periods, with the negative effect of emission reduction factors increasing under the impact of the pandemic. The findings of this study can support policymakers in developing differentiated mitigation strategies at the provincial level to promote the sustainable transformation of waste management in China.

Study on spatial correlation and driving factors of industrial carbon emission in the Hefei metropolitan area based on SNA-QAP method

In this paper, the Hefei metropolitan area is selected as the research object to measure industrial carbon emissions in this area during 2010–2022. The main contribution is to deeply analyze the characteristics of the spatial correlation network of industrial carbon emissions in the Hefei metropolitan area with the modified gravity model and social network analysis(SNA), and to explore the driving factors of its formation with quadratic assignment procedure(QAP). It establishes the foundation for the Hefei metropolitan area to differentiated green city development policies. The results show that (1) The industrial carbon emission in the Hefei metropolitan area showed a fluctuating and rising trend, forming a strongly connected carbon emission network spatial patterna; (2) The overall structure of the spatial correlation network of industrial carbon emissions in the Hefei metropolitan area is stable, but the tightness of the spatial correlation relationship among cities is not enough and the hierarchy is evident; (3) Hefei and Huainan are the centers of the carbon emission spatial correlation network of the Hefei metropolitan area, the position of each city in the carbon emission spatial correlation network is obviously unbalanced; (4) Urbanization level, economic development level, technological innovation, level of opening up and industrial structure exert considerable influence on the establishment and evolution of the spatial correlation network pertaining to industrial carbon emissions within the Hefei metropolitan area.

Spatiotemporal dynamics and driving factors of energy-related carbon emissions in the Yangtze River Delta region based on nighttime light data

Owing to China’s massive area and vastly differing regional variations in the types and efficiency of energy, the spatiotemporal distributions of regional carbon emissions (CE) vary widely. Regional CE study is becoming more crucial for determining the future course of sustainable development worldwide. In this work, two types of nighttime light data were integrated to expand the study’s temporal coverage. On this basis, the distribution of energy-related CE in the Yangtze River Delta (YRD) region of China was estimated at a multispatial scale. Then the spatiotemporal dynamics of CE were explored based on the estimated results. The findings showed that the growth rate of CE in the YRD displayed three stages, and the total CE fluctuated upward. The spatial pattern of CE demonstrated a step-like decline from east to west. However, the Gini coefficient indicated that the differences in CE between cities gradually decreased since the CE had a strong spatial positive correlation in the YRD. Multiple factors affected the spatial variation of CE in the YRD, with economic level and population as the “critical” influencing elements, which determined the absolute amount of CE. This study provides a long-term analysis of CE dynamics while enhancing explanatory accuracy. The methods offer novel perspectives and tools for regional CE research. The results reveal spatial heterogeneity and clustering patterns of CE within regions, contributing valuable scientific evidence for monitoring regional CE and formulating emission reduction policies.

Dynamic Simulation and Reduction Path of Carbon Emission in “Three-Zone Space”: A Case Study of a Rapidly Urbanizing City

Understanding the current and future net carbon emission trajectories in “Three-Zone Space” is crucial for China to promote the formation of a low-carbon development pattern in territorial space and realize carbon neutrality. Taking Wuhan as the study area, we developed carbon emission and sequestration inventories for “Three-Zone Space”. Key driving factors of net carbon emissions were analyzed using the logarithmic mean division index, and future emissions and sequestration under six scenarios were projected with a system dynamics model. The optimal emission reduction pathway was identified through the intelligent decision-making index analysis. Our results show that Wuhan’s net carbon emission increased from 18.589 Mt in 2000 to 42.794 Mt in 2020. The emissions during this period primarily came from urban production space and urban living space. Economic development is the primary factor contributing to the increase in net carbon emissions (36.412 Mt). The efficiency of territorial space utilization is the strongest mitigator of net carbon emissions, reducing net carbon emissions by 74.341 Mt (accounting for 42.06% of total emissions). The comprehensive scenario is the most effective for net carbon emission reduction in urban and ecological spaces, while the technological progress scenario provides the greatest reduction potential in agricultural spaces. These findings provide actionable insights for optimizing spatial planning, enhancing ecological restoration, and adopting low-carbon agricultural technologies to achieve targeted emissions reductions in “Three-Zone Space”. The results of this study can further provide scientific basis for the formulation of targeted emission reduction measures for “Three-Zone Space” and guide the construction of low-carbon territorial space patterns.

Identifying influencing physical and environmental parameters on fate and characterization factors for microplastic emissions in the marine environment

Identifying influencing physical and environmental parameters on fate and characterization factors for microplastic emissions in the marine environment

Carbon Emission Accounting and LMDI Factor Analysis of Fossil Energy in Henan Province

The cultivation of fossil energy consumption in Henan Province between 2005 and 2022 is key to formulate carbon reduction strategies. This study uses the Logarithmic Mean Divisia Index (LMDI) method to decompose the factors of carbon emissions. The total carbon emissions from fossil energy in Henan Province amounted to 3.075 billion tons of CO2, showing an initial increase followed by a decrease, with fluctuations ranging from 125.59 million tons to 209.98 million tons. The LMDI analysis results show that the total effect of fossil energy carbon emissions fluctuated over time. From 2006 to 2011, the total effect was 84.39 million tons. From 2012 to 2019, it was -56.70 million tons. rom 2020 to 2022, it was 14.93 million tons. The greatest positive impact is the economic factor, accounting for 52.36% of the total effect. The greatest negative impact is the technological factor, accounting for 46.32%.Based on this, the study proposes the following carbon reduction strategies: adjusting the energy structure, fostering green technological innovation, and enhancing energy consumption management.

Leaf Size Determines Damage- and Herbivore-Induced Volatile Emissions in Maize.

Stress-induced plant volatiles play an important role in mediating ecological interactions between plants and their environment. The timing and location of the inflicted damage is known to influence the quality and quantity of induced volatile emissions. However, how leaf characteristics and herbivore feeding behaviour interact to shape volatile emissions is not well understood. Using a high-throughput volatile profiling system with high temporal resolution, we examined how mechanical damage and herbivore feeding on different leaves shape plant-level volatile emission patterns in maize. We then tested feeding patterns and resulting consequences on volatile emissions with two generalist herbivores (Spodoptera exigua and Spodoptera littoralis), and assessed whether feeding preferences are associated with enhanced herbivore performance. We found maize seedlings emit more volatiles when larger leaves are damaged. Larger leaves emitted more volatiles locally, which was the determining factor for higher plant-level emissions. Surprisingly, both S. exigua and S. littoralis preferentially consumed larger leaves, and thus maximize plant volatile emission without apparent growth benefits. Together, these findings provide an ecophysiological and behavioural mechanism for plant volatile emission patterns, with potentially important implications for volatile-mediated plant-environment interactions.

Inventory optimisation in a two-echelon cold chain: Sustainable lot-sizing and shipment decisions under carbon cap emissions regulations

Abstract Cold chains are a major source of carbon emissions because they make use refrigerated trucks and warehouses for distribution and storage of inventory. Consequently, inventory management in cold chains should prioritise both profitability and environmental sustainability. This paper optimises inventory in a cold chain system consisting of a single warehouse and retail outlet, responsible for storing and selling cold items, respectively. To maintain product quality and safety, these items must be transported and stored in temperature-controlled environments. Two inventory models for the proposed cold chain are formulated and compared under two distinct carbon emissions regulations, namely, cap-and-trade and cap-and-offset, using both financial and environmental metrics. The proposed models factor in carbon emissions from refrigerated trucks and warehouses used for transporting and storing cold inventory. This study examines both a numerical example and a real case study involving a frozen seafood supply chain. The numerical analysis reveals that cap-and-trade regulation is more effective in reducing environmental impact, leading to 4.4% lower carbon emissions compared to carbon cap-and-offset regulation. However, from an economic standpoint, carbon cap-and-offset regulation performs marginally better, generating a 1% higher profit. The study identifies three key factors that significantly influence both financial and environmental outcomes in the cold chain: truck capacity, fuel efficiency, and freezer performance. As a result, management can best improve profitability and reduce emissions in cold chain operations by focusing on optimising these critical elements.

The Internal Heterogeneity of Carbon Emissions in Megacities: A Case Study of Beijing, China

Cities are of wide concern to scholars due to their major share of global carbon emissions. Energy-related carbon emissions differ significantly among cities, especially megacities, due to regional heterogeneity in socioeconomic conditions. To analyze the differences in influencing factors on carbon emissions within megacities, and further target emission reductions, measures were developed. Beijing was selected to investigate influencing factor differences in the core zones, developing zones and ecological zones using the STIRPAT model on the county level. The results show the following: (1) Regional heterogeneity existed in Beijing in carbon emissions changes from 2010 to 2022. The carbon emissions of the core zones grew steadily and were demonstrated as a major part of Beijing. (2) There were variations in the influencing factors of emissions. Population size was the major driving factor of emissions in the core zones, while emissions in the developing zones and ecological zones were driven primarily by GDP per capita. Notably, urbanization promoted the increase in carbon emissions in the developing zones, but had a negative influence on emissions in the ecological zones. The energy intensity was the primary negative force of carbon emissions in the three zones. (3) The differences in population, economic scale, industrial structure and technological level lead to the heterogeneity of carbon emissions in Beijing. The three zones should formulate targeted emission reduction measures based on the primary factors of carbon emissions and their functional positioning.

MLP Enhanced CO2 Emission Prediction Model with LWSSA Nature Inspired Optimization

Environmental degradation due to the rapid increase in CO₂ emissions is a pressing global challenge, necessitating innovative solutions for accurate prediction and policy development. Machine learning (ML) techniques offer a robust approach to modeling complex relationships between various factors influencing emissions. Furthermore, ML models can learn and interpret the significance of each factor’s contribution to the rise of CO2. This study proposes a novel hybrid framework combining a Multi-Layer Perceptron (MLP) with an enhanced Locally Weighted Salp Swarm Algorithm (LWSSA) to address the limitations of traditional optimization techniques, such as premature convergence and stagnation in locally optimal solutions. The LWSSA improves the standard Salp Swarm Algorithm (SSA) by incorporating a Locally Weighted Mechanism (LWM) and a Mutation Mechanism (MM) for greater exploration and exploitation. The LWSSA-MLP framework achieved a prediction accuracy of 97% and outperformed traditional optimizer-based MLP models across several evaluation metrics. A permutation feature significance analysis identified global trade, coal energy, export levels, urbanization, and natural resources as the most influential factors in CO₂ emissions, offering valuable insights for targeted interventions. The study provides a reliable and scalable framework for CO₂ emission prediction, contributing to actionable strategies for sustainable development and environmental resilience.

A novel DEA-Tobit-SD assessment framework and application of provincial-level carbon emission embracing regional heterogeneity

Formulating tailored emission reduction policies for each Chinese province is crucial due to regional differences in carbon emission evolution patterns. This paper proposes a novel and comprehensive research framework that integrates data envelopment analysis (DEA), Tobit regression, and system dynamics (SD) model to analyze the influence factors and evaluate provincial emission reduction policies while considering regional differences. The DEA method assesses each province's development resource allocation and carbon emission efficiency. Based on the DEA results, each provinces’ key emission influencing factors can be derived combining with Tobit regression and sensitivity analysis of SD. Policies are then selected based on these factors to gauge their effectiveness. SD method is used to simulate carbon emissions under different policy scenarios in the future. The analysis results present obvious differences in resource allocation and regional characteristics among provinces. Qinghai's emission reduction potential has been preliminarily explored as an example. Energy structure, industry structure, energy intensity, forest coverage, and R&D input intensity are its main influencing factors for carbon emission. The forest carbon sink plays a significant role. The emission reduction of the integrated scenario is not a linear sum of all other scenarios. To ensure the completion of the neutralization goal, further adjustments to the long-term policy and extra measures are needed.

Spatial-temporal Evolution and Influencing Factors of Land Use Carbon Emissions in Shandong Province

Exploring the spatial-temporal evolution characteristics of land use carbon emissions and their influencing factors is of great significance for the optimization of land use structure, the formulation of emission reduction policies, and the development of a regional low-carbon economy. Based on land cover and energy consumption data, a multi-parameter land use carbon emission accounting system was constructed to calculate land use carbon emissions in Shandong Province. Moreover, the spatial-temporal evolution and influencing factors of land use carbon emissions were analyzed based on the Gini coefficient and logarithmic mean Divisia index. The results indicated that ① From 2000 to 2020, construction land, woodland, and water area showed a generally increasing trend, while cropland, grassland, and unused land area showed a decreasing trend. The spatial change in land use types was mainly concentrated in the conversion of cropland into construction land （1.09×104 km2）. ② In the study period, carbon emissions from land use in Shandong Province increased from 52.70 million tons to 352.97 million tons, and the annual growth rate decreased from 13.40% to 7.28%, construction land was the primary carbon source, and cropland sequestered carbon. ③ Land use carbon emissions showed significant spatial imbalance at a grid level （Gini > 0.5）, with an overall spatial pattern of "high in the east" and "low in the west." ④ Economic development was the dominant leading factor for the growth of carbon emissions from land use in Shandong Province （contribution rate of 121.33%）, followed by the construction land area effect （33.39%）, while energy structure （-11.12%）, energy efficiency （-20.16%）, and population size effects （-23.44%） limited the increase in carbon emissions.

Spatial-temporal characteristics and influencing factors of farmland carbon emissions in Guangdong Province, China

Agricultural carbon emissions account for 17% of total greenhouse gas emissions in China. To effectively address the eco-environment changes in farmland, which serves as the foundation of agricultural activities, it is essential to estimate regional farmland carbon emissions. This study calculated the farmland carbon emissions in Guangdong from 2011 to 2021 using the classical IPCC carbon emission calculation methodology. The decoupling characteristics betweem farmland carbon emissions and agricultural output values were analyzed utilizing a decoupling model, and the influencing factors were examined through the Logarithmic Mean Divisia Index (LMDI). The results indicate that: 1) Farmland carbon emissions in Guangdong decreased by 13.21% from 2011 to 2021, with pesticide reductions contributing the most to emission decreases. Chemical fertilizers were the largest contributor to farmland carbon emissions, accounting for approximately 61.78% of the total. 2) The spatial distribution of farmland carbon emissions followed the pattern of “Western Guangdong > Northern Guangdong > Eastern Guangdong > Pearl River Delta”. While emission intensity generally declined, regional disparities widened. 3) Most cities in Guangdong exhibited a strong decoupling relationship between farmland carbon emissions and agricultural output values, with decoupling coefficient ranging from −1.182 to −0.004. However, Heyuan and Shenzhen demonstrated a weak decoupling relationship. 4) The primary driver of increased farmland carbon emissions in Guangdong was the level of agricultural output, while improvements in agricultural production efficiency were the most significant inhibitory factor, followed by changes in the scale of agricultural labor force. This study offers policy recommendations to promote the reduction and sequestration of farmland carbon emissions in Guangdong.

Spatial Correlation Network and Influencing Factors of Carbon Emissions from Inter-provincial Tourism Transportation in China

Clarifying the spatial correlation network structure from tourism transportation carbon emissions and its influencing factors is crucial for China's tourism and transportation industry to coordinate the planning of carbon reduction governance and realize the sustainable development of the tourism transportation industry. Based on inter-provincial panel data from 2001 to 2021, China's carbon emissions from tourism transportation were measured, and the modified spatial gravity model was used to construct characteristics of provincial spatial networks and their influencing factors, which were analyzed using the social network analysis method and the QAP model. The study showed that ① China's total carbon emissions from tourism and transportation have been growing slowly year by year, showing a distribution pattern of "high in the southeast and low in the northwest," with obvious differences between the eastern and western regions. ② China's carbon emissions from tourism and transportation formed a multi-threaded and complex network of "dense in the east and sparse in the west." The "Matthew effect" in the spatial network was obvious, with eastern provinces such as Beijing, Shanghai, and Guangdong dominating the core and the northwestern and northeastern provinces such as Xinjiang, Qinghai, Heilongjiang, and Liaoning on the periphery. ③ China's carbon emissions from the tourism transportation block model had a clear division structure, and each block had a large number of correlations and received a spatial overflow of carbon emissions from other blocks. ④ Transportation energy intensity and transportation structure had a significant positive effect on the spatial correlation network, while spatial geographic distance, residents' consumption level, and tourism economic efficiency had a significant negative effect on the spatial network.

Effect of microplastics on soil greenhouse gas emissions: A global meta-analysis study.

Microplastics (MPs) emerged as a critical global pollutant, yet their effects on soil greenhouse gas (GHG) emissions remain uncertain. This meta-analysis evaluates the effects of MPs exposure on GHG emissions and identifies key influencing factors. Regardless of any influencing factors, MPs exposure decreased N2O emissions by 28.5%, while increased CO2 and CH4 emissions by 0.07% and 28.6%, respectively. However, these changes were statistically insignificant. The factors such as MPs concentration, shape, and type, initial soil type and pH, and the presence of additional additive were identified to significantly influence N2O emission response. The most substantial increase in N2O emissions occurred when MPs exposed to silt soils (+55.8%), whereas the greatest inhibition was observed in soils with fertilizer addition (-48.8%). CO2 emission response was significantly influenced by MPs size and shape, initial soil pH and type, experimental duration, and co-additives, with the MPs exposure in sand soils exhibiting the highest increase (+20.7%) and the exposure of fiber MPs causing the largest reduction (-40.4%). Additionally, MPs shape and initial soil pH and type were found to significantly affect CH4 emission response. The model selection analysis revealed that the response ratio [ln (RR)] of nirS gene to MPs exposure and MPs concentration were the most critical factors influencing N2O emissions, whereas the size, concentration, and type of MPs, ln (RR) of Chitinase, initial soil pH, additional additive, and experimental duration were the most important influencing factors for CO2 emissions. Overall, this study highlights the high uncertainty associated with the response of GHG emission to MPs exposure due to the complex interplay of abiotic and biotic processes mediated by MPs under varying conditions. In the future, extensive studies across diverse conditions of MPs (e.g., type, shape, and concentration) and soil (e.g., texture and pH) are still urgently needed.