Total Emissions Research Articles

Underestimated industrial ammonia emission in China uncovered by material flow analysis.

Underestimated industrial ammonia emission in China uncovered by material flow analysis.

Digital Economy, Entrepreneurship of Small and Medium-Sized Manufacturing Enterprises, and Regional Carbon Emissions: Evidence from Chinese Provinces

In recent years, the digital economy (DE) has gained significant attention for its potential in reducing carbon emissions (CE). This paper intends to explore the regional carbon reduction effect of the DE and the entrepreneurship of small and medium-sized manufacturing enterprises (SMMEs), as well as disclose the mechanism through which the entrepreneurship of SMMEs functions. To this end, this paper employs an extended STIRPAT model to analyze the panel data of 30 provinces in China spanning from 2011 to 2018. The empirical analysis shows that (1) the DE has a positive effect on reducing regional total carbon emissions (TCE) and carbon emissions intensity (CEI); (2) the entrepreneurship of SMMEs has a negative influence on reducing regional CE; (3) the entrepreneurship of SMMEs fully mediates the link between the DE and TCE and partially mediates the relationship between the DE and the CEI; and (4) the DE has a stronger carbon reduction effect in regions with low urbanization levels and low institutional quality, as well as non-industrial pilot areas. The findings provide empirical evidence to policymakers on promoting CE reduction and the DE. This study has practical value for SMMEs to improve competitiveness and survival under the current environment.

Impacts of meteorology and precursor emission change on PM2.5 and O3 and identification of synergistic emission reduction pathway: A case of combined pollution event in Beijing, China.

Impacts of meteorology and precursor emission change on PM2.5 and O3 and identification of synergistic emission reduction pathway: A case of combined pollution event in Beijing, China.

Quantifying abrasion of microplastics from mountain bike tires.

Quantifying abrasion of microplastics from mountain bike tires.

Process analysis-based industrial production modelling with uncertainty: A linear fractional programming for joint optimization of total caron emissions and emission intensity

Process analysis-based industrial production modelling with uncertainty: A linear fractional programming for joint optimization of total caron emissions and emission intensity

Unmasking the Environmental Costs of In-Person General Surgery Residency Interviews.

Unmasking the Environmental Costs of In-Person General Surgery Residency Interviews.

The Role of Industrial Energy Efficiency in Emission Reduction: Insights from China

Industrial energy efficiency is a pivotal strategy for mitigating greenhouse gas (GHG) emissions, particularly in industrialized nations like China, where the industrial sector accounts for over 70% of total emissions. This study analyzes China's progress in reducing emissions through technological upgrades, energy management systems, and policy interventions. Employing a comprehensive review of industrial energy data and policy frameworks from 2005 to 2024, the findings reveal a 40% reduction in industrial energy intensity and a 32% decrease in CO₂ emissions per unit of output. Key measures, including combined heat and power systems, renewable energy integration, and ISO 50001-certified systems, have been instrumental, supported by targeted programs for energy-intensive enterprises and financial incentives. However, challenges such as coal dependency, regional disparities, and limited digitalization persist. China’s efforts present a valuable model for achieving carbon neutrality by 2060 and offer insights for other nations seeking to balance industrial growth with sustainability. The findings underscore the critical role of energy efficiency in combating climate change while highlighting areas for future research and policy development.

Characterizing full-phase chlorine species emissions from domestic coal combustion in China: Implications for significant impacts on air pollution and ozone-layer depletion.

Characterizing full-phase chlorine species emissions from domestic coal combustion in China: Implications for significant impacts on air pollution and ozone-layer depletion.

Microplastics in German paper mills' wastewater and process water treatment plants: Investigation of sources, removal rates, and emissions.

Microplastics in German paper mills' wastewater and process water treatment plants: Investigation of sources, removal rates, and emissions.

Assessing the Environmental Sustainability of Organic Wine Grape Production with Qualified Designation of Origin in La Rioja, Spain

Vineyards are significant demanders of fertilisers, pesticides, soil tillage and water. This study assessed the environmental profile of an organic grape production system with La Rioja qualified designation of origin using a cradle-to-gate life cycle assessment (LCA). The ReCiPe method was applied to assess the environmental impacts, while the Available WAter REmaining method was used to estimate the water scarcity. Additionally, the biodiversity loss, a global issue exacerbated by agricultural practices, was evaluated along with an ecosystem service indicator, pollination, to provide a more comprehensive analysis. This study employed two functional units: one kilogram of grapes and one hectare of land. The results revealed that the environmental impacts on global warming were more than ten times lower than those reported in most studies reviewed in the literature, primarily due to the effects of direct land use changes associated with pruning waste management. The total emissions in this category were 99.51 kg CO2 eq per hectare or 15.31 g CO2 eq per kilogram of grapes. Agrochemical-related emissions were identified as the environmental hotspot. The water scarcity was estimated at 48.4 litres per kilogram of grapes, mainly attributed to agrochemical dispersion. The biodiversity loss was largely driven by land transformation, with plants being the most impacted taxon. However, a high abundance of pollinators was observed in spring, contributing to improved grape quality and natural pest control. These findings could help highlight the environmental benefits of organic viticulture and the good practices implemented in this pilot.

Estimation of Greenhouse Gas Emissions and Analysis of Driving Factors in Jiangxi Province’s Livestock Industry from a Life Cycle Perspective

As a significant source of greenhouse gas emissions, objectively understanding the quantity of emissions from the livestock industry and their spatiotemporal evolution is crucial for advancing low-carbon and green development in regional livestock industries. Therefore, based on the Life Cycle Assessment (LCA) method, this study estimated greenhouse gas emissions from the livestock industry across 11 municipal regions in Jiangxi Province from 2002 to 2022, revealing the spatiotemporal characteristics of these emissions. The study also employed the Logarithmic Mean Divisia Index (LMDI) model to analyze the influencing factors. The results showed that (1) between 2002 and 2022, total greenhouse gas emissions from Jiangxi Province’s livestock industry exhibited an upward trend, increasing from 13.52 million tons to 21.27 million tons, with an average annual growth rate of 2.36%. (2) During the study period, the spatial patterns of super-high-emission and light-emission zones remained relatively stable, while medium and heavy emission zones showed dynamic evolution. (3) Intensity effects, agricultural structural effects, and rural population size played a suppressive role in livestock greenhouse gas emissions, while regional development levels and urbanization levels were key drivers of increased emissions. Therefore, governments should accelerate the implementation of clean production models, enhance technological innovation, promote pollution reduction at the source, and develop differentiated livestock development strategies based on regional resource endowments and demographic–economic characteristics.

A Study on Measures to achieve Net Zero and RE100 goals in Urban Water Cycle System

Urban water cycle systems(UWCS), including water treatment facilities, distribution facilities, sewers, and wastewater treatment facilities, are energy intensive and significant source of greenhouse gas (GHG) emissions, making the reduction of GHG emissions and the transition to eco-friendly energy essential. This study identifies specific GHG emission sources at each stage of the UWCS and proposes detailed methods to achieve a 40% reduction in GHG emissions, implement RE100, and attain Net Zero by employing insets and offsets. This study develops scenarios for insets and offsets based on the baseline process of the UWCS, and investigates potential pathways to reduce GHG emissions by quantifying emissions from each process. Internal insets, which are self-implemented and technical measures, are prioritized, while external offsets are applied to compensate for the remaining emissions. Internal insets include the application of anaerobic digesters and combined heat and power(CHP), improvements in energy efficiency of equipment, reduction in water pipe leakage, implementation of water footprint labeling, and installation of on-site photovoltaic system. External offsets comprise renewable energy certificates(REC), power purchase agreements(PPA), green hydrogen fuel for vehicles, natural sequestration improvement, and emission trading system. GHG emissions at each stage within the UWCS are quantified using modeling software. Based on these results, the effectiveness of insets and offsets in achieving a 40% GHG emissions reduction, Net Zero, and RE100 goal is analyzed. The baseline total GHG emissions for the UWCS are estimated at 4,732.8 tCO<sub>2eq</sub>/yr, of which 56.8% is identified as targets for internal insets, and the remaining 43.2% is reduced through external offsets. A 40% GHG reduction can be achieved through internal insets, and Net Zero can be attained by incorporating additionally applying external offsets. The total power demand of UWCS facilities and equipment is calculated as 572.8 kW. Renewable energy is generated through anaerobic digesters and CHP(116.1kW) as well as on-site PV(395.0 kW), while RE100 compliance is achieved by securing an aditional 61.7 kW through REC/PPA. Achieving Net Zero and RE100 requires prioritizing strategies for insets, offsets and efficient resource allocation. For this, the technical feasibility and self-implementation potential of reduction efforts and the external conditions for offsets, should be carefully reviewed to optimize implementation strategies. GHG reduction and renewable energy utilization in the UWCS are key priorities for addressing the climate crisis and achieving sustainable water resource management, requiring technological innovation and institutional support. The comprehensive and systematic application of GHG insets and offsets is the optimal approach to achieving these goals. Furthermore, modeling software serves as a key tool for quantifying GHG emissions and formulating concrete, viable GHG reduction strategies. In addition to the technical and institutional approaches proposed in this study, achieving Net Zero and implementing RE100 requires the integrated consideration of economic factors in the future.

Business Strategies and Carbon Emissions: The Role of Direct and Indirect Carbon as Mediators

The increasing insistence of climate change has made carbon emissions a decisive factor in business strategy, affecting financial performance and business competitiveness. This study explores the relationship between business strategies and carbon emissions, focusing on mediating the roles of direct and indirect carbon emissions. This study is based on Miles and Snow’s (1978) classification of business strategies, which is Defend and Prospect which affects carbon emissions. The study employs structural equation modeling (SEM) regression to examine the influence of firm size, profitability, and cash flow stability and leverage their relation with carbon emissions. Prospect takes a value of 1 if the business’s strategy score is between 24 and 30; otherwise, it is 0. Whereas the Defend variable equals 1 when the strategy score is between 6 and 19; otherwise, 0. As larger businesses release more carbon emissions hence, firm size is incorporated into the regression. The role of CO2 emissions work as a mediator in strategies of businesses, which remains an important and central area of research. By successfully reducing direct emissions; firms may frequently gain efficiency and cost advantages, whereas addressing indirect emissions improve sustainability across the value chain. The study uses the main dependent variable, the natural logarithm of total CO₂ equivalent emissions (Scope 1 and Scope 2), to determine the direct and indirect discharges. As major operations are the main characteristic of the prospector hence, the findings suggest that they release more carbon emissions, contrary to defenders. Low CO2 emissions are positively linked to profitability and stable cash flows as businesses with strong monetary positions engage in more sustainability initiatives. Though, leverage has a negative connection with carbon exposure, highly leveraged firms prioritize financial commitments over environmental concerns (Yasin et al., 2023). Besides this, firm size plays a crucial role, as huge firms generate more emissions due to high operational systems. Reducing carbon emissions could be essential for businesses aiming to adopt a more aggressive (prospect) From a policy and stakeholder perspective, these insights hold significant implications. Policymakers must establish regulatory frameworks that encourage carbon reduction initiatives while maintaining financial viability for firms.

Spatiotemporal Patterns and Influencing Factors of Carbon Emissions in the Yangtze River Basin: A Shrinkage Perspective

This study categorizes 45 cities into four types based on population dynamics using census data (2000–2020). Methods such as ArcGIS10.8, carbon emission estimation, LISA clustering, and association analysis are employed to explore the spatiotemporal distribution of shrinking cities and carbon emissions. This study analyzes carbon emission patterns and influencing factors for the four city types and provides policy recommendations. The findings are as follows: (1) Lasting-growth cities show a “two-end mass, middle-point” pattern, while stage-growth and stage-shrinking cities are “point” distributed. Lasting-shrinking cities are mainly distributed in the middle and lower reaches of the Yangtze River. (2) Total carbon emissions are rising, showing two clusters of high-value areas. Carbon emission intensity is falling quickly, being higher in the west and lower in the east. (3) Lasting-growth cities have the fastest direct carbon emission growth rate, stage-growth cities have the fastest energy-related indirect emission growth rate, and cities undergoing population increase have the fastest growth rate for other indirect carbon emissions. In terms of carbon reduction, lasting-growth cities perform best, whereas stage-growth cities perform worst. (4) Regional GDP, per capita regional GDP, urban construction area, and hospital beds per 10,000 people promote carbon emission reduction in the four city types, while a higher number of industrial enterprises inhibits it. Birth rate, aging rate, and mortality rate have no significant impact. This study addresses the gaps in previous research on shrinking cities and carbon emission reduction by considering the dynamic nature of shrinking processes and analyzing carbon emission patterns.

Environmental impact of colorectal cancer screening with colonoscopy and multi-target stool DNA (mt-sDNA) testing.

The substantial carbon footprint imparted by medical services warrants increased attention to their environmental impact. National guideline organizations such as the US Preventive Services Task Force (USPSTF) recommend multiple modalities for average-risk colorectal cancer (CRC) screening with varying resource intensity. The aim of this study was to quantify the environmental burden for 2 of the most used CRC screening modalities, colonoscopy and the multi-target stool DNA (mt-sDNA) test. A validated CRC microsimulation model was used to estimate the number of screening and follow-up tests for a cohort of 1 million average-risk individuals who underwent screening between ages 45 and 75. Component resources used for mt-sDNA, including waste products, energy, and transportation for colonoscopy and mt-sDNA, were collected from January 1, 2023, to January 1, 2024, and converted to carbon-equivalent emissions. Resources used for colonoscopy were captured from the literature. Resources devoted to screening colonoscopy were substantially (59%) higher than those to mt-sDNA, even when including follow-up colonoscopy. Of note, follow-up colonoscopy accounted for the majority (64%) of total emissions for the mt-sDNA screening strategy. Compared with colonoscopy screening, mt-sDNA substantially reduces the carbon emissions attributable to population-level CRC screening. Environmental impact should be included as a factor when choosing among guideline-recommended CRC screening strategies.

Quantitative Identification of Emission Sources and Emission Dynamics of Pressure-Relieved Methane Under Variable Mining Intensities

This study addresses the abnormal emission of pressure-relieved methane under high-intensity mining conditions by integrating geostatistical inversion, FLAC3D-COMSOL coupled numerical simulations, and stable carbon–hydrogen isotopic tracing. Focusing on the 12023 working face at Wangxingzhuang Coal Mine, we established a heterogeneous methane reservoir model to analyze the mechanical responses of surrounding rock, permeability evolution, and gas migration patterns under mining intensities of 2–6 m/d. Key findings include the following: (1) When the working face advanced 180 m, vertical stress in concentration zones increased significantly with mining intensity, peaking at 12.89% higher under 6 m/d compared to 2 m/d. (2) Higher mining intensities exacerbated plastic failure in floor strata, with a maximum depth of 47.9 m at 6 m/d, enhancing permeability to 223 times the original coal seam. (3) Isotopic fingerprinting and multi-method validation identified adjacent seams as the dominant gas source, contributing 77.88% of total emissions. (4) Implementing targeted long directional drainage boreholes in floor strata achieved pressure-relief gas extraction efficiencies of 34.80–40.95%, reducing ventilation air methane by ≥61.79% and maintaining return airflow methane concentration below 0.45%. This research provides theoretical and technical foundations for adaptive gas control in rapidly advancing faces through stress–permeability coupling optimization, enabling the efficient interception and resource utilization of pressure-relieved methane. The outcomes support safe, sustainable coal mining practices and advance China’s Carbon Peak and Neutrality goals.

Design of digital low-carbon system for smart buildings based on PPO algorithm

Currently, the carbon emissions from buildings account for one-third of the total global carbon emissions. Therefore, how to control the carbon emissions of buildings becomes the main direction of current research. The study aims to address the issue of poor energy regulation and carbon emission control effectiveness in the current digital low-carbon system for smart buildings. A smart building digital low-carbon new system based on proximal policy optimization algorithm is proposed in the research. The new system uses a near-end strategy optimization algorithm to control the energy changes of buildings, and adds an actor-critic algorithm to enhance the evaluation and analysis ability of system strategies. The research results indicate that improving the near-end strategy optimization algorithm can reduce carbon emissions by 2354CO2e, while the lowest operating cost of the model is only 35,000 yuan. The new system can effectively control building costs, reducing commercial building thermal energy costs by 1.1w and industrial building electrical energy costs by 17,000 yuan. The highest reliability of the new system is 92.4%, the highest stability is 93.6%, and the shortest response time of the system is 768 ms. The results show that the new system is superior to the existing system in reducing carbon emissions, lowering operating costs, improving system reliability and stability, and shortening response time. This demonstrates the effectiveness and superiority of the new system in the digital low-carbon system of intelligent buildings. Using the novel system can effectively decline the carbon emissions of buildings and improve the control effect on building energy. This has good guiding significance for improving the low-carbon emission control effect of smart buildings.

Optimized Dispatch of Integrated Energy Systems in Parks Considering P2G-CCS-CHP Synergy Under Renewable Energy Uncertainty

To enhance low-carbon economies within Park Integrated Energy Systems (PIES) while addressing the variability of wind power generation, an innovative optimization scheduling strategy is proposed, incorporating a reward-and-punishment ladder carbon trading mechanism. This method effectively mitigates the unpredictability of wind power output and integrates Power-to-Gas (P2G), Carbon Capture and Storage (CCS), and Combined Heat and Power (CHP) systems. This study develops a CHP model that combines P2G and CCS, focusing on electric-heat coupling characteristics and establishing constraints on P2G capacity, thereby significantly enhancing electric energy flexibility and reducing carbon emissions. The carbon allowance trading strategy is refined through the integration of reward and punishment coefficients, yielding a more effective trading model. To accurately capture wind power uncertainty, the research employs kernel density estimation and Copula theory to create a representative sequence of daily wind and photovoltaic power scenarios. The Dung Beetle Optimization (DBO) algorithm, augmented by Non-Dominated Sorting (NSDBO), is utilized to solve the resulting multi-objective model. Simulation results indicate that the proposed strategy increases the utilization rates of renewable energy in PIES by 28.86% and 19.85%, while achieving a reduction in total carbon emissions by 77.65% and a decrease in overall costs by 36.91%.

Impact of the Type of Energetic Material on the Fume Emission in Open-Pit Mining

This study examines the fume emissions from various energetic materials utilized in open-pit mining, emphasizing the influence of chemical composition on their environmental impact. The analysis of fume emissions based on data from an open-pit mine reveals that the annual consumption of approximately 89.7 tons of ANFO, 121.4 tons of emulsion, or 137.8 tons of dynamite can result in total COx and NOx emissions ranging between 16,432.88 and 21,834.07 m3. The use of TNT boosters in ANFO and emulsion energetic material further amplified emissions; however, substituting TNT with dynamite for priming achieved a notable reduction in overall fumes by approximately 9–9.5%, depending on the energetic material used. The scale effect of energetic material mass highlighted the importance of optimized formulations for large-scale blasting. A three-year predictive model indicated fluctuations in energetic material demand, with reductions anticipated as deposits deplete. The result of this study offers pathways for reducing emissions and process optimization, particularly in large-scale mining operations, where the blasting technique is the major extraction method.

Big Data and Satellite Imagery for Energy Efficiency Mapping in Indonesia:

In the sophisticated realm of big data, analyzing energy efficiency in Indonesia has become crucial for identifying savings opportunities. This study utilizes large-scale raster data, including CO2 emissions from the OCO-2 GEOS satellite, nocturnal satellite images from VIIRS, and demographic and infrastructural data from WorldPOP and EsriWorld Cover. Through advanced regression techniques in machine learning—Support Vector Regression, Artificial Neural Network, and particularly Random Forest—the research analyzes and forecasts energy efficiency across various Indonesian provinces. The analysis highlights a notable increase in CO2 emissions from 2019 to 2023, with a significant reduction in night-time light emissions in 2020 due to the pandemic, which temporarily decreased human activities. Despite these fluctuations, the continuous increase in population density and built-up areas underscores the persistent influence of urbanization on emissions. The Random Forest model, which provided the most accurate predictions, indicates an expected rise in total CO2 emissions until 2030, driven by urbanization and economic growth, followed by a decline by 2045 due to targeted governmental policies. These insights contribute significantly to understanding the distribution of energy efficiency and support the development of sustainable energy policies in Indonesia. The study not only enriches scientific literature but also guides policy-making, offering a framework for tailored energy efficiency improvements. This research marks a pivotal advancement in utilizing big data and satellite technology to optimize energy use in a context that was previously underexplored.