Towards farm-level net-zero greenhouse gas emissions: Contributions of climate mitigation actions - A study of four European crop and dairy farms.

Fluorinated greenhouse gases (F-GHGs) from the semiconductor industry represent a rapidly growing climate driver, yet localized accounting and abatement analyses remain scarce. To improve estimation accuracy and assess mitigation potential, we developed an improved accounting model by incorporating previously neglected sources, experimentally measuring destruction and removal efficiencies (DREs) for major process gases, and establishing gas usage coefficients by wafer size and process type. Applied to Shanghai's semiconductor sector in 2022, the model estimated 3.35 Mt CO2e emissions, dominated by process gases (47.93%) and electricity consumption (43.88%). Emissions based on measured DREs were 7.4-23.0 times higher than those from the Intergovernmental Panel on Climate Change (IPCC) 2019 default DREs, revealing overestimation in defaults. The share of indirect emissions increased markedly with wafer size, from 20.68% to 55.46%, driven by higher power intensity in advanced nodes and greater abatement efficiency. Plasma-based abatement achieved the highest DREs, yet their NOx byproducts warrant further attention. Without additional controls, emissions could increase to 4.20 Mt CO2e by 2035, whereas defined mitigation scenarios could achieve 37.49-77.69% reductions, primarily through enhanced gas abatement and low-carbon electricity adoption. The study emphasizes localized methodology and scenario analysis as essential for developing effective climate strategies in emerging industries.

The reality of rising greenhouse gas emissions and global temperatures underscores the urgency of transitioning to a net-zero energy system. Modern radiotherapy contributes to the total healthcare domain emissions. As the global incidence of cancer rises, expanding treatment capabilities is critical. However, sustainability in high-emission healthcare disciplines is equally paramount. This paper explores the academic knowledge base and practical strategies for understanding and managing emission contributions to align with sustainability goals. The European Union's Corporate Sustainability Reporting Directive and climate disclosure frameworks in Australia and New Zealand require certain organisations, including large hospitals and radiotherapy networks, to report their direct and indirect emissions. Climate disclosure frameworks emphasise transparency and resilience to climate risks, supported by methodologies such as the Greenhouse Gas Protocol and ISO 14064. The study narratively examines existing literature and practical solutions through engaged scholarship and provides insights into modelling radiotherapy emissions guided by the principles of the Corporate Sustainability Reporting Directive. Radiotherapy emissions are a small but significant share of healthcare emissions, with indirect emissions dominating. They are estimated at 6.75 Mt. CO2e, accounting for approximately 0.3% of global healthcare emissions. While sustainable construction and innovative technologies reduce upstream impacts, downstream greenhouse gas reductions remain limited despite travel minimisation efforts. By advancing sustainability in radiotherapy, this work contributes to the broader healthcare sector's efforts to mitigate climate impacts and aligns with international climate commitments.

Abstract Previous studies found that households’ energy use and consumption of goods and services contribute greatly to increasing carbon emissions. This study estimated household carbon footprints, including direct and indirect emissions. Using the 2019 Taiwan Survey of Family Income and Expenditure, we investigated the heterogeneous relationship between household income and carbon emissions. Income sources were divided into six categories: compensation of employees, entrepreneurial income, property income, imputed rent income, transfer income, and miscellaneous income. Our results indicate that the income–emission elasticity for household total emission, direct emission, and indirect emission are 0.62, 0.37, and 0.84, respectively. Household income is positively associated with household carbon footprint, especially among households with higher imputed rent income. The proportion of household indirect emissions rises as income increases. Household income exhibits a stronger relationship with indirect carbon emissions than with direct carbon emissions. These findings suggest an important avenue for reducing the carbon footprint of high-income households: promoting energy-saving measures and encouraging the use of low-carbon footprint goods and services. Examples include those produced with recycled materials or low-carbon energy sources.

The study conducted a detailed carbon footprint assessment of the Gandhi Krishi Vignana Kendra (GKVK) campus, University of Agricultural Sciences, Bangalore, using internationally recognised standards such as the Intergovernmental Panel on Climate Change (IPCC) Guidelines and the greenhouse gas (GHG) Protocol. As institutions are increasingly recognised for their contribution to GHG emissions through energy use, transport, waste and food services, this research quantified emissions across scope 1 (direct), scope 2 (indirect from purchased electricity) and scope 3 (other indirect emissions). Activity data were collected from institutional records, surveys and field assessments, while emission factors were sourced from IPCC (6th assessment report) AR6, the united nations framework convention on climate change (UNFCCC) GHG calculator v2.6 and Central Electricity Authority (CEA) guidelines. The total GHG emissions from the GKVK campus were estimated at 7606.545 tonnes CO₂e/yr, with scope 1 contributing 42.08 %, scope 2 contributing 20.46 % and scope 3 contributing 37.46 %. Major sources included liquefied petroleum gas (LPG) usage (1334884 kg CO₂e), grid electricity (1556180 kg CO₂e), food waste (1622855 kg CO₂e), refrigerant leakage (982430 kg CO₂e) and student commuting (879466 kg CO₂e). While the study provides a comprehensive institutional GHG baseline, it is limited to one academic year and excludes embodied emissions from infrastructure. The findings establish a replicable framework for Indian educational institutions to measure, manage and mitigate emissions. Future research should extend this model to multi-campus assessments and long-term carbon management planning, strengthening the roadmap toward carbon-neutral academic ecosystems and supporting India’s broader net-zero commitments.

Towards the minimum equivalent carbon emission of soil thermal desorption remediation by post-combustion carbon capture.

Quantifying the spatiotemporal characteristics and driving mechanisms of greenhouse gas emissions from the wastewater treatment sectors in Chinese cities.

Dill is a valuable herb recognized for its rich nutritional composition and bioactive properties. Drying is an efficient preservation technique for maintaining its quality characteristics and ensuring longer storage stability. Incorporating ultrasonic pretreatment before the drying process can significantly reduce energy consumption (SEC) and greenhouse gas emissions. To the best of our knowledge, this is the first study to comprehensively evaluate ultrasound-assisted hybrid microwave–convective drying of dill (Anethum graveolens L.) leaves, focusing on the combined effects on drying kinetics, energetic and exergetic performance, providing an indirect emission estimate and multiple quality attributes. This study aimed to evaluate the drying kinetics, energy and exergy performance parameters, greenhouse gas emissions, quality properties (water activity, rehydration ratio and color) and antioxidant capacity of dill leaves dried by the microwave–hot-air (MW-HA) technique combined with ultrasonic (US) pretreatment. The experiments were conducted at MW power levels of 20%, 40%, and 60% (corresponding to a total output of 900 W), air temperatures of 40 and 60 °C, and US pretreatment durations of 0, 10, and 30 min. The results illustrated that rising MW power, air temperature and US duration significantly reduced the drying time, SEC and greenhouse gas emissions. At higher process conditions, specifically, 40% MW power, 60 °C drying temperature, and 30 min US pretreatment, the maximum energy efficiency (10.17%) and exergy efficiency (11.35%) were obtained. In terms of quality attributes, the best results were achieved at 40% MW power, 60 °C air temperature, and 10 min ultrasonic pretreatment, with reduced water activity (0.258), minimal color variation (ΔE = 11.44), improved rehydration ratio (3.88), and high retention of antioxidant activity. These findings demonstrate the potential of ultrasound pretreatment to enhance drying performance by reducing energy use and emissions while improving quality and antioxidant retention in dill, offering new guidelines for sustainable processing of this herb. Future studies should optimize microwave–hot-air-drying conditions to balance energy efficiency, exergy, and product quality.

This study tracks the transformation of China’s household carbon footprints from 2002 to 2022, examining urban-rural disparities and regional heterogeneity. Integrating multiregional input–output modeling and structural decomposition analysis, we quantify direct and indirect household emissions and disentangle the roles of fuel switching, energy efficiency, consumption growth, production structure, and interprovincial trade. Results reveal divergent decarbonization trajectories: while aggregate household emissions plateaued after 2017, urban emissions peaked and declined whereas rural emissions continued rising, driven by ongoing energy transitions and lifestyle changes. Residential and transportation sectors have overtaken food as dominant emission sources, signaling fundamental lifestyle shifts. Meanwhile, growing spatial decoupling between consumption and production highlights intensifying interprovincial emission transfers, with affluent regions increasingly outsourcing carbon footprints to industrial provinces. These findings call for differentiated strategies: accelerating rural electrification, strengthening urban demand-side management, and establishing mechanisms to balance production – consumption responsibilities – all essential for China's dual carbon goals.

The purpose of the research is to develop mathematical models for assessing the green aspects of railway logistics and identifying optimal sustainable solutions to minimize the carbon footprint and increase resource efficiency in the context of a circular economy. Methodology. The article examines the concept of green logistics in railway transport through the prism of environmental, economic and social aspects of sustainable development. The calculation of the carbon footprint of rail logistics is based on the ISO 14083:2023 standard and the concept of green logistics. The results. According to the green logistics methodology, the full carbon footprint includes not only direct emissions from operation, but also indirect emissions from energy production, infrastructure construction and waste management. Mathematical models have been developed to calculate the carbon footprint of rail transport and optimize energy consumption taking into account the principles of the circular economy. A comprehensive green sustainability index for rail logistics, GMSL (Green Multimodal Sustainability for Logistics), which integrates indicators of decarbonization, resource efficiency and social responsibility, has been proposed. The modeling results demonstrate that the implementation of green logistics principles through electrification with renewable energy sources can reduce CO₂ emissions by 90%, and the use of hydrogen locomotives ensures complete decarbonization of transportation. A comparative analysis of six scenarios for the development of green railway logistics for Ukraine was conducted, which revealed that the highest integrated sustainability is achieved with a mixed strategy that includes multimodal integration, green freight corridors and digital transformation. Scientific novelty. An improved mathematical model for calculating the carbon footprint for different types of railway traction was developed, a model for optimizing rail transport energy consumption was built and studied. An analysis of international experience showed the effectiveness of the proposed approaches as a complete transition to renewable energy sources (Netherlands), mass implementation of hydrogen technologies (Germany, France), integration of solar energy (India, Great Britain), digitalization of management (Switzerland, Japan). A comprehensive index of green sustainability of railway logistics with the integration of circular economy principles was proposed. Practical significance. A phased strategy is recommended for Ukraine: completion of electrification of main corridors by 2027, achievement of 75% electrification by 2030, integration of 30% renewable energy sources by 2030, launch of hydrogen locomotives and full decarbonization by 2040. The possibility of using the developed models to justify investment decisions on the modernization of the railway network of Ukraine and to assess the environmental effect of the implementation of various technological solutions is proven.

Exploring the spatio-temporal pattern， dynamic evolution， and carbon compensation zoning of the county land use carbon budget has great practical significance for territorial spatial pattern optimization and building a fair and effective regional carbon compensation mechanism in the Beijing-Tianjin-Hebei Region. Based on land use cover， nighttime light， and energy consumption data， a land use carbon budget estimation model was constructed in the Beijing-Tianjin-Hebei Region area. Based on the exploratory spatial data analysis method and kernel density estimation， the spatio-temporal pattern and dynamic evolution trend of carbon emissions were analyzed. The economic value of carbon offsetting was calculated based on the carbon offsetting value model. Finally， carbon compensation zoning was carried out based on K-means clustering combined with ecological function zoning. The results indicated that： ① The fitting R2 of the indirect carbon emission estimation model for construction land was 0.776 8， with good simulation accuracy and the estimation effect meeting the expected standards. ② The carbon sink in the Beijing-Tianjin-Hebei Region showed a continuous growth trend； the carbon source showed a decreasing trend after peaking； and the overall increase in net carbon emissions was consistent with the carbon source and exhibited significant positive spatial correlation， with four types of aggregation patterns. ③ The absolute difference in net carbon emissions between districts and counties in the Beijing-Tianjin-Hebei Region as a whole， Tianjin， and Hebei Province showed a trend of expansion， while the absolute difference in net carbon emissions between districts in Beijing shifted from expansion to a trend of narrowing. The tailing effect in the Beijing-Tianjin-Hebei region as a whole and the three provinces was quite significant. ④ In 2022， Xicheng and Dongcheng districts in Beijing and Heping district in Tianjin were key areas for payment. Chongli district in Zhangjiakou， Daxing district in Beijing， and Fengning Manchu Autonomous County in Chengde were key areas for compensation. The compensation areas-restricted development zones included 76 districts and counties， which was the type with the highest proportion， mainly concentrated in most districts and counties of the Yanshan-Taihang Mountain ecological conservation area and the Bashang Plateau ecological protection area in Hebei Province.

As a core metric for climate policy, the scientific estimation of carbon social costs is crucial for formulating mitigation strategies. However, traditional integrated assessment models predominantly focus on the global aggregate, failing to adequately account for regional heterogeneity, sectoral characteristics, and strategic interactions between regions. They also lack systematic integration of ESG principles. To address this, this paper examines regional and sectoral carbon social costs driven by ESG development. Through cooperative and non-cooperative games, we improve the integrated economic-environmental-climate development model, take the eight economic regions in China as an example, get the carbon social cost of each economic region and typical important industries, and obtain the key parameters and the evolution law of carbon social cost. The model categorizes the carbon emissions after the implementation of emission reduction policies under the ESG perspective into direct and indirect emissions. It studies the economic impacts of the two types of emissions before and after the implementation of emission reduction policies, and conducts research on the top four typical important industries (industry, construction, transportation, and power) that rank among the top four global CO2 emitters, to obtain the analytical solution of the social cost of carbon in the region and the typical important industries. In addition, this paper numerically simulates the social cost of carbon for the four industries under the baseline scenario, cooperative game scenario, non-cooperative game scenario, and temperature limitation scenario. The study shows that the social cost of carbon in the northern, southern and eastern coastal economic regions is higher than that in other economic regions, the social cost of carbon in the industrial and electric power industries in each economic region is higher than that in the building and transportation industries, and the more stringent the temperature limit is, the higher the social cost of carbon is in the economic regions.

The global rise in dialysis treatments for kidney failure patients has led to growing concern about the carbon footprint of dialysis and the environmental implications of increased consumption of key resources, including water, electricity, and the manufacture and waste disposal of treatment-related consumables. As most environmental studies have reported from temperate climates, we examined the carbon footprint of a hemodialysis center in Singapore, which represents a country with a tropical climate, aiming to assess total carbon emissions quantitatively, systematically identify key components, and provide critical insights along with pragmatic recommendations for enhancing sustainability practices. We measured the direct and indirect carbon emissions using the Greenhouse Gas Protocol Product Standard at our 12-station satellite dialysis center. This study was conducted at the outpatient satellite dialysis center of NUH between October 2023 and January 2024. This 12-station satellite dialysis facility operates three shifts daily, six days a week, and encompasses an area of 210 m². It is equipped with twelve machines, supplied with dialysis water from AquaB Duo Water Treatment System (Fresenius Medical Company, Bad Homberg, Germany). Acid concentrates (dilution 1:34, 5L containers) and bicarbonate powder are supplied in single-use plastic containers. The direct and indirect greenhouse gas (GHG) emissions were measured retrospectively using the GHG Protocol Product Standard, developed by the World Resources Institute and the World Business Council for Sustainable Development. Our center dialyzed approximately 60 patients each month, resulting in a total of 8932 sessions over the course of the year. Our total annual emissions were evaluated at 358.52 tonnes of carbon dioxide (tCO₂e), with an average carbon emission of 5.98 tCO₂e per patient. Most of these emissions were due to dialysis consumables, accounting for 147.50 tCO₂e (41.1%). The dialysis consumables contributing the most to carbon emissions were dialyzers, bicarbonate powder, bloodlines, dialysis needles, and acid concentrates. This was followed by electricity, clinical supplies, and pharmaceuticals, which contributed 55.27 tCO₂e (15.4%), 24.35 tCO₂e (6.8%), and 36.24 tCO₂e (10.1%), respectively. Waste generated accounted for 30.94 tCO₂e (8.6%) of the carbon emissions. Our study on the carbon footprint of a satellite hemodialysis unit is the first one conducted in an equatorial urban city environment, experiencing high humidity and abundant rainfall, with a tropical climate. Further research into local carbon emissions across other centers in Southeast Asia is essential for validating our findings and informing sustainability policies.

Carbon footprint of coke-making in Europe and the cost-effectiveness of plant design: Optimization by using alternative reductants.

Towards operational net-zero pathways in industrial wastewater treatment systems using digital decision support tools.

N2O emissions fueled by eutrophication in a shallow lake.

Carbon dioxide ( CO 2 ) levels in our atmosphere continue to reach new historic highs every day. In 2022, industry made up 30% of United States greenhouse gas emissions, including electricity end-use indirect emissions. Simultaneously, personal consumption expenditures increased by 9.2% in 2022 and continue to rise, indicating that there is an increasing need for decarbonization in production processes, with a tandem effort to combat overconsumption. One class of technologies that has garnered interest from researchers, policymakers, and communities is carbon capture. “Point source carbon capture” specifically refers to capturing CO 2 at its point of emission before it is released to the environment. However, as further scrutiny is put on carbon capture technology, it is clear that further research is needed and more options are necessary to ensure that our goal of reducing greenhouse gas emissions is not done without care. Carbon capture and sequestration or utilization have been touted as possible solutions for our growing demands, but present a range of health, safety, and environmental challenges, including siting concerns and possible leakages of pipelines or storage facilities. A relatively newer technology, reactive carbon capture (RCC), provides a potential pathway that minimizes some of those concerns. RCC converts CO 2 to valuable fuels and chemicals immediately after it is captured, eliminating the need for energy-intensive desorption from capture agents, as well as CO 2 storage and transport through pipelines. Thus, the entire process is contained at existing industrial plant sites, minimizing the span of environment and frontline communities that may be impacted by potential leaks and pollution. However, there are also a number of remaining challenges in RCC technology that must be addressed, alongside the need to decrease consumption, in order to be in accordance with environmental justice principles and make way for safe and effective technology adoption.

Under the “dual carbon” strategy and green energy transition, traditional static accounting models have coarse temporal granularity. These models cannot meet the needs of fine-grained management and dynamic control for industrial parks. Therefore, it is urgent to develop high-resolution dynamic accounting systems and analyze model uncertainty. This study first defines the carbon source structure and establishes the accounting boundary for industrial parks. Second, it proposes dynamic accounting methods for both direct and indirect carbon emissions. Third, the study develops an uncertainty analysis model that considers parameter variability and error propagation. Finally, the feasibility and effectiveness of the proposed method are validated through a case study of a typical industrial park in Sichuan Province, China. The results indicate that the overall uncertainty of carbon emissions in the park is 28.9%, with electricity consumption identified as the primary driver of uncertainty (Spearman correlation coefficient of 0.986). The proposed framework effectively captures real-time emission fluctuations, providing a scientific basis for fine-grained carbon management.

Climate change has intensified the need for financial innovation to support sustainable transitions. Green bonds have emerged as key instruments to fund low carbon investments and promote environmental accountability. This study investigates how environmental, social, and governance (ESG) performance and carbon emission intensity affect the proportion of green bonds issued by Asian firms from 2019 to 2023. Results show that ESG score is negatively correlated with the proportion of green bonds, meaning that companies with higher sustainability credibility are less dependent on external green financing. Carbon emission intensity is positively associated with green bond proportion, indicating that firms with higher indirect emissions are more likely to use green bonds to demonstrate environmental commitment and enhance legitimacy. From sub-sample ASEAN and non-ASEAN firms highlight the role of institutional contexts in shaping sustainable finance adoption. These findings contribute by reinterpreting signaling theory and legitimacy theory within the Asian context, showing that firms with high ESG credibility refrain from costly green bond signaling due to established reputation, while firms with higher carbon emission intensity actively issue green bonds as legitimacy tools to maintain social and investor acceptance.

Background: Achieving deep decarbonization in global supply chains is essential for advancing net-zero objectives; however, the integrative role of artificial intelligence (AI) and robotics in this transition remains insufficiently explored. This study examines how these technologies support carbon-emission reduction across supply chain operations. Methods: A curated corpus of 83 Scopus-indexed peer-reviewed articles published between 2013 and 2025 is analyzed and organized into six domains covering supply chain and logistics, warehousing operations, AI methodologies, robotic systems, emission-mitigation strategies, and implementation barriers. Results: AI-driven optimization consistently reduces transport emissions by enhancing routing efficiency, load consolidation, and multimodal coordination. Robotic systems simultaneously improve energy efficiency and precision in warehousing, yielding substantial indirect emission reductions. Major barriers include the high energy consumption of certain AI models, limited data interoperability, and poor scalability of current applications. Conclusions: AI and robotics hold substantial transformative potential for advancing supply chain decarbonization; nevertheless, their net environmental impact depends on improving the energy efficiency of digital infrastructures and strengthening cross-organizational data governance mechanisms. The proposed framework delineates technological and organizational pathways that can guide future research and industrial implementation, providing novel insights and actionable guidance for researchers and practitioners aiming to accelerate the low-carbon transition.