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The publishing industry, a major contributor to greenhouse gas emissions, produced approximately 730 Mt CO2eq globally in 2020 during the paper production phase alone. Unlike other sectors, decarbonization in publishing requires systematic reforms across the supply chain, production efficiency, energy transitions, consumption patterns, and recycling processes, as reliance on renewable energy alone is insufficient. This study focuses on China's physical publishing industry, developing a comprehensive, high-resolution carbon emissions dataset that spans multiple publication types, stages, and processes. It reveals the emission characteristics across the life cycle, aiming to quantify the emissions accurately and address the lack of life-cycle-based research. This study explores efficient, replicable, and scalable strategies to facilitate the industry's low-carbon transformation and sustainable development. The findings are as follows. (1) Books are the primary carbon emissions source, contributing approximately 77.05% of the total emissions, while journals and newspapers account for 13.20% and 9.75%, respectively. (2) Annual carbon accounting across the life-cycle identifies paper production and printing as the most carbon-intensive stages, responsible for about 85% of the total emissions. (3) In terms of recycling efforts, carbon reductions of approximately 347,000 t CO2eq per year can be achieved through measures such as waste paper and plastic packaging recycling, second-hand publication exchanges, and energy recovery from incineration. [ABSTRACT FROM AUTHOR]

Copyright of Industrial Minerals & Processing / Huagong Kuangwu yu Jiagong is the property of Industrial Minerals & Processing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Metal Working (1674-165X) is the property of Metal Working Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Artificial Intelligence (AI) tools based on Machine learning (ML) have demonstrated their potential in modeling climate-related phenomena. However, their application to quantifying greenhouse gas emissions in cities remains under-researched. Here, we introduce a ML-based bottom-up framework to predict hourly CO2 emissions from vehicular traffic at fine spatial resolution (30 × 30 m). Using data-driven algorithms, traffic counts, spatio-temporal features, and meteorological data, our model predicted hourly traffic flow, average speed, and CO2 emissions for passenger cars (PC) and heavy-duty trucks (HDT) at the street scale in Berlin. Even with limited traffic information, the model effectively generalized to new road segments. For PC, the Relative Mean Difference (RMD) was +16% on average. For HDT, RMD was 19% for traffic flow and 2.6% for average speed. We modeled seven years of hourly CO2 emissions from 2015 to 2022 and identified major highways as hotspots for PC emissions, with peak values reaching 1.639 kgCO2 m−2 d−1. We also analyzed the impact of COVID-19 lockdown and individual policy stringency on traffic CO2 emissions. During the lockdown period (March 15 to 1 June 2020), weekend emissions dropped substantially by 25% (−18.3 tCO2 day−1), with stay-at-home requirements, workplace closures, and school closures contributing significantly to this reduction. The continuation of these measures resulted in sustained reductions in traffic flow and CO2 emissions throughout 2020 and 2022. These results highlight the effectiveness of ML models in quantifying vehicle traffic CO2 emissions at a high spatial resolution. Our ML-based bottom-up approach offers a useful tool for urban climate research, especially in areas lacking detailed CO2 emissions data. [ABSTRACT FROM AUTHOR]

Climate policy – though shaped by international regimes – has traditionally been heavily domestic in practice. Accordingly, public emissions accounting frameworks have been designed to support policies targeting emitting processes within the territory, and to facilitate the production of National Accounts. Now, a combination of emissions leakage, competitiveness, and both investor and consumer concerns are driving the rapid emergence of policies targeting emissions embodied in traded products. To support these product-targeted policies, governments are investing in the development of public embedded emissions accounting frameworks (EEFs). While EEFs as an information instrument have enormous potential to support the transition to a net-zero global economy, they equally have the potential to inhibit trade, slow the transition, and have a disproportionate impact on developing countries – both through their design, and through potential incompatibilities between accounting developed in different jurisdictions. This article contributes to addressing these challenges by describing the minimum viable set of principles for an emissions accounting information instrument to be compatible with both international trade law and climate change mitigation regimes. To identify these design principles, our method is a systematic review of carbon accounting and trade law literature. Noting the prominent place of principles in guidelines for carbon accounting practice, we additionally extract principles identified in leading emissions accounting guidelines. Theory-based identification and analysis of these systematically extracted principles is used to develop the synthesized set of key principles for the design of public embedded emissions accounting schemes for traded products. Public agencies developing approaches for product emissions accounting, such as to meet requirements for carbon border adjustment mechanisms, could use these principles to guide design and negotiation. Key policy insights Public embedded emissions accounting frameworks (EEFs) are rapidly emerging policy instruments which sit at the intersection of the international climate and trade regimes. Development of a common set of design principles will be important to ensure that these frameworks support the goals of both regimes; specifically climate change mitigation and freer trade. Key principles from carbon accounting are accuracy, monotonicity, transparency, relevance, and conservativeness. Key principles from trade law are non-discrimination, least restrictive means, and subsidiarity. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Resources Development & Market is the property of Sichuan Resource Development & Market Magazine Co., Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Managing vegetation to sequester carbon in biomass requires estimates to meet standards for accuracy, with methods that are transparent, verifiable and cost‐effective. Allometric models are commonly used to predict biomass from non‐destructive field inventory data. Although a number of studies have addressed biomass error propagation, none have provided a general set of methods for linking errors all the way from initial allometric model development through to the final site‐based biomass prediction, for both above‐ and below‐ground biomass. Error sources in total biomass (above‐ + below‐ground) were quantified using a combination of analytical and Monte Carlo methods, illustrated with four contrasting case studies using either site‐ and‐species‐specific, species‐specific or generalised allometric models. Sampling error was found to be the most important contributor to site‐level biomass uncertainty, arising from the interaction between spatial variability and the field sampling design. The contribution of allometric model covariance to total error was also quantified, with errors in the determination of moisture content during allometric model development identified as a potentially important yet often overlooked error source. Application of different allometric models to the same inventory data suggested the error from generalised models was no greater than that from site‐ or species‐specific models, with increases in the generalised model prediction error balanced by decreases in other error sources associated with the increased sample size on which generalised models are based. Recommendations for reducing errors in predicted biomass include increasing field survey sample size, adopting field survey designs that ensure spatial representativeness and improving moisture content measurement protocols and increasing the moisture content sample size during allometric model development. To reduce costs while maintaining acceptable accuracy, the use of generalised allometric models is recommended, with the caveat that additional biomass sampling for model validation may be required to limit the potential for biased predictions.

The k-nearest neighbors (kNN) algorithm is a versatile tool for mapping forest attributes. However, the effects of using inadequate reference plots for modeling have not been thoroughly investigated. The interaction between topographic and biological factors results in a more complex distribution of forest attributes, leading to significant uncertainty and challenges in obtaining reliable information. This study presents a protocol that uses a 4-way factorial experimental design to establish appropriate sampling schemes aimed at reducing uncertainty and bias in estimating aboveground biomass (AGB) using the kNN technique. The research was conducted in a mixed forest within a subtropical region affected by wildfires, pine wilt disease, and agricultural activities. A total of 252 sampling schemes, incorporating various sampling methods, feature counts, neighbor counts (k), and reference-target distances (RTD), were utilized to generate corresponding AGB-kNN models. The performance of the models was assessed against measured AGB using a tree-based IPCC-compliant method with a high-resolution orthoimage and canopy-height-model data. Results indicated that these sampling schemes produced AGB estimations with average error rates ranging from 13% to 241%. The optimal kNN model utilized spectral, biophysical, and topographic features through a systematic sampling approach, with k set at 30 and RTD at 900 meters. Findings suggest that systematic sampling outperformed random and cluster sampling, with models that used moderate k and RTD generally providing less biased estimates. This protocol effectively identifies suitable kNN-AGB models, enhancing the ability to delineate areas with low biomass productivity for precision management, while also supporting forest improvement initiatives and biomass-related studies.

Excessive greenhouse gas emissions lead to global warming, which poses a critical challenge for human society. Worldwide consensus that carbon emissions must be reduced to achieve sustainable development has raised research interest on the topic of carbon footprint, an assessment standard for measuring greenhouse gas emissions. To shed light on this important topic, we introduce carbon footprint-related international standards and domestic policies, and conduct quantitative and qualitative analyses to provide a comprehensive overview of the current carbon footprint literature. Specifically, through a bibliometric analysis of 13 203 carbon footprint publications circulated over the past decade, we quantify the publication volume, influence, and collaboration networks of countries/regions, institutions, and journals. The results show that publications on carbon footprint have been increasing since 2013, and more so after 2017. Regarding domain influence, China and its academic institutions show a strong influence. In addition, a cluster analysis of the keywords of all publications shows seven categories, encompassing carbon footprint accounting methods and research priorities. Furthermore, a qualitative analysis of the research methods and subjects related to carbon footprint reveals the need to increase communication and collaboration among different disciplines and countries/regions to strengthen the construction of carbon footprint systems. Accordingly, this study identifies popular topics and development trends in the carbon footprint literature to provide research direction for promoting green and low-carbon transformation of the social economy to achieve sustainable development.

Abstract Human activities have been driving massive greenhouse gas emissions, causing global warming, and triggering increasingly frequent extreme weather events that severely threaten the environment. Power generation is the leading contributor to anthropogenic emissions, making precise, real‐time measurement and monitoring of power plant carbon emissions crucial in reducing climate change. This study uses a new sophisticated pipeline that combines tropospheric monitoring instrument satellite data, power plant attributes, and advanced artificial intelligence algorithms to build a predictive carbon emission model. The approach utilizes multimodal data processing, encoding, and model optimisation. Experimental results confirm that this pipeline can automatically extract and utilize vast amounts of relevant data, thereby enabling the artificial intelligence model to accurately predict power plant carbon emissions and providing a vital tool for reducing global warming.

Carbon dioxide removal (CDR) systems are an integral part of sustainable pathways limiting global warming to less than 2.0 °C. When the sole purpose of CDR is capturing and storing atmospheric CO2, carbon registries offer detailed procedures to calculate the carbon removal credits. However, the registries do not address how to distribute CO2 flows when CDR provides additional services. Standardized, transparent rules for distributing CO2 flows among CDR services are required for the formation of efficient private and public carbon markets. The lack of such rules could result in double counting if those reductions are allocated to more than one service, decreasing the trustworthiness of carbon removal credits or deterring the delivery of an additional low-carbon service, thus limiting the economic viability and deployment of CDR. We examine allocation rules in carbon registries and carbon accounting guidelines, including their life cycle assessment (LCA) principles. We evaluate physical (mass-based) and non-physical (economic) allocation methods using a generic CDR system and find both to be unworkable. We then develop a mass balance (MB) approach which can reliably allocate captured and stored carbon (CSC) between carbon removal credits and other services based on the value CO2 removal in those markets. This practical approach to allocation can be used in a transparent way to provide flexibility that would allow CDR services to capture the value of the multiple services they provide and, through this, promote the deployment of these sustainable alternatives. [ABSTRACT FROM AUTHOR]

This study examines the use of blockchain technology in ESG reporting, with a focus on carbon emissions accounting within supply chains. Using a design science methodology, it develops a blockchain-based framework that integrates automated data collection, verification, and analysis. The framework enables precise tracking of carbon emissions, enhances compliance with ESG standards, and resolves key issues like data reliability and standardization. Results show improved reporting accuracy through immutable data records and automated verification processes. This advancement allows organizations to optimize decision-making, reduce carbon footprints, and achieve sustainability goals. The study contributes theoretically by introducing an innovative blockchain-integrated ESG reporting model and practically by offering a validated framework for companies to streamline carbon accountability and improve ESG compliance. [ABSTRACT FROM AUTHOR]

Summary: Net zero as a policy for reducing atmospheric carbon emissions is relatively straightforward; however, the implementation of that policy is not, particularly in difficult-to-measure sectors such as agriculture. As strategies to reduce emissions are explored, multiple uncertainties in measuring these emissions are confronted. In this paper, we use the example of a coffee supply chain in Peru to illustrate the magnitude of potential variability in emissions accounting results, which represent a necessary first step in moving towards net zero. We show that scope boundaries and emissions factors chosen for carbon calculations significantly alter emissions outcomes and can result in discrepancies of over 77 million kg CO2e when scaled to a medium-size coffee trader. Net zero targets and efforts to reduce emissions may be over- or understated depending on subjective decisions that cause significant differences in emissions results. Although framework guidance exists, it is apparent that a greater set of micro-level agreements is needed for calculating the emissions of lesser-studied sectors, such as agricultural supply chains. This process is imperative to focus efforts on reducing emissions and on moving from net zero as a mere policy to action and implementation. [ABSTRACT FROM AUTHOR]

Soils are the largest terrestrial reservoir of organic carbon, yet they are easily degraded. Consistent and accurate monitoring of changes in soil organic carbon stocks and net greenhouse gas emissions, reporting, and their verification is key to facilitate investment in sustainable land use practices that maintain or increase soil organic carbon stocks, as well as to incorporate soil organic carbon sequestration in national greenhouse gas emission reduction targets. Building up on an initial review of monitoring, reporting and verification (MRV) schemes with a focus on croplands, grasslands, and forestlands we develop a framework for a modular, scalable MRV system. We then provide an inventory and classification of selected MRV methodologies and subsequently "score" them against a list of key characteristics. It appears that the main challenge in developing a unified MRV system concerns the monitoring component. Finally, we present a conceptual workflow that shows how a prototype for an operational, modular multi-ecosystem MRV tool could be systematically built. [ABSTRACT FROM AUTHOR]

Individuals utilizing Life Cycle Assessment within the emerging field of circular carbon might phrase and understand matters differently, causing ambiguity, due to different educational backgrounds and lacking definitions of central terms. Our research addresses the question, whether misleading or ambiguous terms exist, which impair transparency and scientific accuracy in carbon (dioxide) accounting in LCA. A structured literature research was performed to screen LCA standards, ISO standards, and the European legal database EUR-Lex. Nine terms with potential ambiguity in meaning or usage in LCA were identified: Avoided burden, avoided carbon, avoided CO2, carbon capture, carbon credit, circular carbon, circular CO2, negative emission, and net zero emission. A workflow was introduced to either obtain a consensus-based definition from standard literature or from peer-reviewed publications. For all terms, except avoided carbon, a recommended definition was found and showcased within case studies on the topic of mechanical recycling of plastics to produce a taxonomy suggestion for precise descriptions of different carbon dioxide (equivalent) accounting methods in LCA. [ABSTRACT FROM AUTHOR]

Today's global net-zero governance landscape fails to recognize some of the most important strategic actions companies can take to mitigate climate change. Standards have been built primarily to guide companies in setting targets (e.g. through the Science Based Targets initiative) and to help companies' track emissions reductions in their annual inventories (e.g. using the Greenhouse Gas Protocol). As guidance is updated, debates have arisen about how companies can report efforts to reduce wider emissions in society (such as carbon credits purchased and avoided emissions from the use of products). In response, we review how a focus on emissions reductions within the value chain is critical but insufficient for recognizing and rewarding the full scale of opportunities companies have for changing wider systems. In addition to inventory reporting and target setting across the greenhouse gas 'Scopes', a second reporting track is needed to compare and reward companies' efforts across their 'Spheres of Influence'. This article presents a framework offering a meaningful and separate place for companies to report efforts to leverage (a) their products, to empower others to avoid emissions; (b) their purchasing power and (c) their policy advocacy. Recognising and thus rewarding entities' dedication to broader societal interventions will be critical for achieving an economy-wide transition to global net zero. [ABSTRACT FROM AUTHOR]

The aim of this study is to calculate the carbon emission amounts and emission costs of an enterprise operating in the iron and steel sector on the basis of activities and products with the Activity Based Costing (ABC) System. Thus, the calculation of the share of carbon cost in operating expenses with the ABC system is considered to be an important factor that can affect the preferences of the management in product production decisions. The main reason for the selection of the iron and steel sector in this study is the significant impact of the sector on carbon emissions on a global scale; since this sector causes approximately 7% of carbon emissions worldwide (International Energy Agency, 2020). The data in the study were obtained from the accounting documents of an enterprise operating in the sector, field research and meetings with the management. In the study, the carbon footprint and carbon costs of each activity centre carried out in the enterprise were determined. While determining the carbon footprints of the activity centres, CO2, CH4 and N2O greenhouse gases determined in the Kyoto Protocol were taken into consideration. The emission factors required to monitor these gases were obtained from Greenhouse Gas (GHG), UK Government GHG Conversion Factors for Company Reporting and previous academic studies in this field. With the help of activity data and emission factors, the amount of carbon emissions occurring in the production activities of the enterprise was determined. In order to calculate the carbon emission cost, the carbon price subject to trading in the Emission Trading System was determined and the costs of carbon emissions collected in the activities were calculated. The findings obtained from the study are as follows: In 2021, 671.39 tonnes of the Raw Material Storage Activity Centre, 1,158.59 tonnes of the Billet Cutting and Furnace Preparation Activity Centre, 27,684.14 tonnes of the Annealing Activity Centre, 6,042.46 tonnes of the Rolling Activity Centre, 1,483.39 tonnes of the Finished Product Cutting Activity Centre, 1.483.39 tonnes, Straightening Activity Centre 3,037.93 tonnes, Finished Product Storage Activity Centre 833.63 tonnes, Maintenance and Repair Activity Centre 1,565.93 tonnes, and Shipment Activity Centre 5,303.64 tonnes, resulting in 47,781.10 tonnes of carbon emissions in total. On the basis of the products produced in the enterprise, it was calculated that UAC Profile 0.882 tonnes, Equilateral Angle Iron 0.663 tonnes, Lama Iron 0.425 tonnes, Square Iron 0.616 tonnes, Round Iron 0.226 tonnes of carbon emissions per unit. As a result of these calculations, the carbon cost incurred by the enterprise is 12,554,484.03 TL. When evaluated on the basis of the products produced in the enterprise, it was determined that UAC Profile 231,75 TL, Equilateral Angle Iron 174,20 TL, Lama Iron 111,67 TL, Square Iron 161,85 TL tonnes, Round Iron 59,38 TL carbon cost. As a result of the study, it is seen that the carbon cost incurred by the enterprise corresponds to approximately 14% of the manufacturing overheads incurred in the enterprise in 2021. [ABSTRACT FROM AUTHOR]

Carbon disclosures are essential for investors to evaluate firms' efforts to combat climate change. In this study, we focus on a specific type of carbon disclosures—namely, emission allowance disclosures—that capture how firms implement emission reductions under the European Union emission trading system (EU ETS). Given the continuously changing institutional features of the EU ETS and the lack of authoritative guidance on how firms should account for emission allowances in their financial statements, it is exceedingly difficult for investors to understand numerical emission allowance disclosures and to compare them across—and even within—firms. Motivated by this criticism, we hypothesize and find that textual disclosures complementing numerical emission allowance disclosures are associated with lower information asymmetries between firms and their investors. Further analyses show that textual disclosures that: (i) describe the accounting approach, and (ii) contain institutional information on the EU ETS, respectively, are particularly important for improving the information environment of numerical emission allowance disclosures. Overall, our findings suggest that text can improve the understanding of numbers in a carbon disclosure context. Therefore, our study not only contributes to the (industrial ecology) literature but also has important implications for regulators, policymakers, investors, financial analysts, and firms. [ABSTRACT FROM AUTHOR]

Increasing social, economic, and political pressure causes many companies to pledge to decarbonize. A common measure involves the use of Energy Attribute Certificates (EAC), such as the European Guarantees of Origin (GO), to reduce emissions from electricity procurement (Scope 2). However, previous studies find no effect on additional renewable energy capacity. Focusing on Norway and Germany as dominant contributors to net GO exports and imports, this study examines the GO trade alongside corporate carbon accounting data to answer the research question: Does the decarbonization of corporate electricity procurement using Guarantees of Origin contribute to the expansion of renewable electricity generation capacity in Norway and Germany? The analysis of CDP and Association of Issuing Bodies data reveals Norway's consumption mix is more carbon intensive than Germany's because Norway exports GO and imports fossil electricity attributes. German companies report predominantly market‐based approach, mainly using green tariffs and GO for zero emission claims, while Norwegian companies favor the location‐based approach. The largest share of GO issued in Norway comes from hydropower plants aged 41 to 70 years. The results highlight the urgency to revise corporate carbon accounting standards. GO lack additionality due to double counting of renewable attributes. Potential solutions include additionality criteria, GO trade restrictions based on physical capacities, and age limits for eligible power plants. This study's novelty is linking CDP data with GO trade, to assess the integrity of corporate decarbonization strategies. It contributes valuable insights to ongoing discourse on the role of EAC. [ABSTRACT FROM AUTHOR]

Long-term forest carbon modelling is helpful in climate change mitigation actions. Estimating potential carbon sequestration in forests can be considered the long-term strategy for low carbon and climate resilience in the National Determination Contribution. In Hungary, the black locust (Robinia pseudoacacia) and poplars (Populus sp.) are prominent and dominant species in reforestation and afforestation projects. The research aimed to estimate the carbon dynamics of black locust and white poplar long rotation plantations in some forest management scenarios. Thirty-six forest management scenarios were developed from two species, six yield classes, and tree wood utilization. CO2FIX modelling projected carbon dynamics for 45 years of rotation. Our findings have shown that class yield I resulted in the highest carbon stock compared to class yields II–VI. Black locust plantations have stored carbon to a larger extent than white poplar plantations. In terms of wood utilization, harvested black locust or white poplar wood contributed the most extensive carbon stock if used for pulp. In conclusion, from 36 forest management scenarios, the best scenario was black locust plantation in class yield I and the aim for pulp that stored the carbon at the end of rotation was 101.75 Mg C/ha. In all forest management scenarios, the soil carbon stock was higher than carbon in biomass, products, and bioenergy. Thus, conserving the soil as the dominant carbon pool is vital for future policy recommendations. [ABSTRACT FROM AUTHOR]

Biochar is the product of intentional pyrolysis of organic feedstocks. It is made under controlled conditions in order to achieve desired physico‐chemical characteristics. These characteristics ultimately affect biochar properties as a soil amendment. When biochar is used for carbon storage, an important property is its persistence in soil, often described by the proportion of biochar carbon remaining in soil after a 100 years (Fperm$$ {\mathrm{F}}_{\mathrm{perm}} $$). We analyzed published data on 1230 biochars to re‐evaluate the effect of pyrolysis parameters on biochar characteristics and the possibility to predict Fperm$$ {\mathrm{F}}_{\mathrm{perm}} $$ from the maximum temperature reached during pyrolysis (HTT). We showed that biochar ash and nitrogen (N) contents were mostly affected by feedstock type. The oxygen to carbon (O:C) and hydrogen to carbon (H:C) ratios were mostly affected by the extent of pyrolysis (a combination of HTT and pyrolysis duration), except for non (ligno)cellulosic feedstocks (plastic waste, sewage sludge). The volatile matter (VM) content was affected by both feedstock type and the extent of pyrolysis. We demonstrated that HTT is the main driver of H:C ‐‐ an indicator of persistence ‐‐ but that it is not measured accurately enough to precisely predict H:C, let alone persistence. We examined the equations to estimate Fperm$$ {\mathrm{F}}_{\mathrm{perm}} $$ available in the literature and showed that Fperm$$ {\mathrm{F}}_{\mathrm{perm}} $$ calculated from HTT presented little agreement with Fperm$$ {\mathrm{F}}_{\mathrm{perm}} $$ calculated from H:C. The sign and magnitude of the bias depended on the equation used to calculate Fperm$$ {\mathrm{F}}_{\mathrm{perm}} $$ and the dispersion was usually large. This could lead to improper compensation of carbon emissions and wrong reporting of carbon sinks in national carbon accounting schemes. We recommend not to use HTT as a predictor for persistence and stress the importance to rapidly develop more accurate proxies of biochar C persistence in soil. [ABSTRACT FROM AUTHOR]

In recent years, with the increase in greenhouse gas emissions, global warming has created a series of complex ecological challenges. Colleges and universities are still in the exploratory stage of constructing low-carbon campuses. This study takes the campus of Zhejiang Ocean University as the research object and obtains carbon emission data from it based on the emission coefficient method by visiting the site and issuing a questionnaire survey. A total of 246 questionnaires were collected in this study, which were quantitatively analyzed to further understand the composition of the carbon emission structure of universities and put forward the corresponding energy-saving and emission-reducing suggestions. This study actively responds to the national call for energy saving and emission reduction and aims to provide a data reference as well as scientific support for the construction of a low-carbon campus. The results show that in 2021, the carbon emissions of Zhejiang Ocean University were 3.39 × 107 kg, of which direct carbon emissions were 3.34 × 105 kg, accounting for 0.99% of the total carbon emissions; indirect carbon emissions were 1.50 × 107 kg, accounting for 44.33% of the total; and other indirect carbon emissions were 1.85 × 107 kg, accounting for the remaining 54.68%. Also, this study found that individuals in colleges and universities pay attention to carbon emissions, but it is difficult for them to maintain a low-carbon approach in daily life. Based on the results of the analysis, this study proposes a series of effective measures to reduce carbon emissions at the campus of Zhejiang Ocean University, unite students in colleges and universities, and fully implement the concepts of energy saving, emission reduction, and sustainable development in college life. [ABSTRACT FROM AUTHOR]

This study highlights the main concerns regarding firms' carbon accounting and decarbonization practices. Under institutional, legitimacy and stakeholder theories, this study analyses the linkages between carbon accounting outputs and integrated reporting (IR), as well as between disclosure assurance systems in the transition towards net‐zero business models (n‐ZBMs). This study emphasizes the awareness of accounting tools to be considered environmental and social measures due to decarbonization practices. This study developed a systematic literature review (SLR) of 433 English articles published in Scopus database and Google Scholar between the years 1990 and 2024. It is a consolidated methodological approach in the 'sustainability accounting' research field to fill the existing gaps that justify this study. The results of this study highlight that IR and carbon accounting awareness enhance firms' performance and have positive social and environmental impacts with the adoption of decarbonization practices towards n‐ZBMs through an improved understanding of stakeholders and regulatory pressures. This study is the first to explore the relationship between carbon accounting awareness and IR about the decarbonization practices, as well as the role of assurance in the transition for n‐ZBMs. This study tries to move from the concept of legitimacy of the decarbonization practices that firms aim to achieve through sustainable disclosure to the legitimacy of business models towards net‐zero emissions. [ABSTRACT FROM AUTHOR]

Background: Many fires in north Australian savannas are severe enough to cause canopy scorch, tree death and removal of stags. Better fire management may increase carbon sequestration in trees, perhaps including stags. Aims: To describe and analyse dynamics of stags in tropical savannas (600–1000 mm annual rainfall) in relation to fire and better understand their role in biomass sequestration. Methods: We monitored marked populations of live and dead trees over 12 years. Statistical models describing influences on stag creation and loss are applied in stag dynamics simulations. Key results: Immediately following severe fire, stag biomass increases acutely because many more live trees are killed than stags removed. Between severe fires, stag losses exceed tree deaths, so peaks are quite short. Many 'new' stags are lost (fallen or consumed) quickly. Conclusions: Between fires, stags comprise ~7.5–8.9% of standing above-ground biomass, more under dry conditions and during recovery from severe fire or other drivers of increased tree mortality. Fire management is unlikely to increase proportions of total woody biomass in stags unless it also reduces live biomass. Implications: Reducing frequency of severe fires can increase total carbon sequestration in dry tropical savannas. Prediction uncertainties and management risks around sequestration present daunting challenges for policy-makers and fire management practitioners. Fire creates stags by killing trees and also removes them. Proportions of stags lost usually exceed per capita mortality rates except after severe fire, when tree deaths greatly outnumber stag losses. Fire management is unlikely to increase the proportion of biomass in stags unless live biomass declines. This article belongs to the Collection Savanna Burning. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Accounting & Financial Studies Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Artificial Intelligence (AI) tools based on Machine learning (ML) have demonstrated their potential in modeling climate-related phenomena. However, their application to quantifying greenhouse gas emissions in cities remains under-researched. Here, we introduce a ML-based bottom-up framework to predict hourly CO2 emissions from vehicular traffic at fine spatial resolution (30 × 30 m). Using data-driven algorithms, traffic counts, spatio-temporal features, and meteorological data, our model predicted hourly traffic flow, average speed, and CO2 emissions for passenger cars (PC) and heavy-duty trucks (HDT) at the street scale in Berlin. Even with limited traffic information, the model effectively generalized to new road segments. For PC, the Relative Mean Difference (RMD) was +16% on average. For HDT, RMD was 19% for traffic flow and 2.6% for average speed. We modeled seven years of hourly CO2 emissions from 2015 to 2022 and identified major highways as hotspots for PC emissions, with peak values reaching 1.639 kgCO2 m−2 d−1. We also analyzed the impact of COVID-19 lockdown and individual policy stringency on traffic CO2 emissions. During the lockdown period (March 15 to 1 June 2020), weekend emissions dropped substantially by 25% (−18.3 tCO2 day−1), with stay-at-home requirements, workplace closures, and school closures contributing significantly to this reduction. The continuation of these measures resulted in sustained reductions in traffic flow and CO2 emissions throughout 2020 and 2022. These results highlight the effectiveness of ML models in quantifying vehicle traffic CO2 emissions at a high spatial resolution. Our ML-based bottom-up approach offers a useful tool for urban climate research, especially in areas lacking detailed CO2 emissions data.

Abstract Carbon peaking and achieving carbon neutrality have emerged as pivotal strategic imperatives in China. These objectives not only drive a shift in production, lifestyle, and consumption patterns but also illuminate a path towards a comprehensive green metamorphosis in China’s economic and social development landscape. The distinctive nature of museums as quintessential public edifices, requiring sustained regulation of temperature and humidity alongside attracting substantial foot traffic, positions them as crucial pioneers in the pursuit of carbon peaking and neutrality. It is essential for museums to leverage their leadership and advocacy roles to catalyze low-carbon construction practices across society. This study delves into the energy dynamics specific to the museum sector, delving into the recycling of exhibition materials, methodologies for carbon footprint assessments of visitors and museum staff, and the establishment of a standardized carbon emission accounting framework tailored to the museum industry. Through meticulous examination of representative cases and subsequent analysis, this research delineates decarbonization strategies, offering indispensable technical scaffolding for carbon assessment and emission reduction efforts within the museum realm.

Abstract Background Wood carbon fractions (CFs)—the proportion of dry woody biomass comprised of elemental carbon (C)—are a key component of forest C estimation protocols and studies. Traditionally, a wood CF of 50% has been assumed in forest C estimation protocols, but recent studies have specifically quantified differences in wood CFs across several different forest biomes and taxonomic divisions, negating the need for generic wood CF assumptions. The Intergovernmental Panel on Climate Change (IPCC), in its 2006 “Guidelines for National Greenhouse Gas Inventories”, published its own multitiered system of protocols for estimating forest C stocks, which included wood CFs that (1) were based on the best available literature (at the time) and (2) represented a significant improvement over the generic 50% wood CF assumption. However, a considerable number of new studies on wood CFs have been published since 2006, providing more accurate, robust, and spatially- and taxonomically- specific wood CFs for use in forest C estimation. Main text We argue that the IPCC’s recommended wood CFs and those in many other forest C estimation models and protocols (1) differ substantially from, and are less robust than, wood CFs derived from recently published data-rich studies; and (2) may lead to nontrivial errors in forest C estimates, particularly for countries that rely heavily on Tier 1 forest C methods and protocols (e.g., countries of the Global South with large expanses of tropical forests). Based on previous studies on this topic, we propose an alternative set of refined wood CFs for use in multiscale forest C estimation, and propose a novel decision-making framework for integrating species- and location-specific wood CFs into forest C estimation models. Conclusion The refined wood CFs that we present in this commentary may be used by the IPCC to update its recommended wood CFs for use in forest C estimation. Additionally, we propose a novel decision-making framework for integrating data-driven wood CFs into a wider suite of multitiered forest C estimation protocols, models, and studies.

Purpose – This study aims to investigate the impact of financial performance on carbon emission disclosure. Design/methodology/approach – The study uses ordinary least squares (OLS) multiple regression analysis on a sample of 177 Financial Times Stock Exchange 350 index (FTSE-350) non-financial firms to test the impact of market (Tobin’s Q) and accounting (return on equity) financial performance indicators on carbon emission disclosure. Findings – The results show that the financial performance market indicator has a significant positive impact on carbon emission disclosure. The accounting indicator illustrates similar results except for Scope 3, where the results are insignificant. This study may help firms understand how financial performance affects carbon emission disclosure, particularly by showing that high-performing firms are motivated to maintain strong environmental practices and enhance carbon emission awareness. Originality/value – This paper enhances stakeholders’ understanding of how firms’ environmental policies align with their financial objectives, thereby expanding knowledge in carbon accounting.

Carbon peaking and achieving carbon neutrality have emerged as pivotal strategic imperatives in China. These objectives not only drive a shift in production, lifestyle, and consumption patterns but also illuminate a path towards a comprehensive green metamorphosis in China's economic and social development landscape. The distinctive nature of museums as quintessential public edifices, requiring sustained regulation of temperature and humidity alongside attracting substantial foot traffic, positions them as crucial pioneers in the pursuit of carbon peaking and neutrality. It is essential for museums to leverage their leadership and advocacy roles to catalyze low-carbon construction practices across society. This study delves into the energy dynamics specific to the museum sector, delving into the recycling of exhibition materials, methodologies for carbon footprint assessments of visitors and museum staff, and the establishment of a standardized carbon emission accounting framework tailored to the museum industry. Through meticulous examination of representative cases and subsequent analysis, this research delineates decarbonization strategies, offering indispensable technical scaffolding for carbon assessment and emission reduction efforts within the museum realm. [ABSTRACT FROM AUTHOR]

Online conferences are widely thought to reduce many of the costs of convening academic communities. From lower carbon emissions, lower fees, less difficulty in attending (particularly for marginalised researchers), and greater accessibility, virtual events promise to address many of the issues that in‐person events take for granted. In this article, we draw on a community economies framing from geographers J.K. Gibson‐Graham to argue for centring the work of convening within efforts to explore reparative possibilities within the academy. Reflecting on the changing costs arising from moving an originally in‐person conference series online, we argue for embracing the opportunities offered. We explore how organising teams might enact alternative values through allocating the material, financial and labour resources traditionally spent for these events differently. We look particularly at how our carbon and financial costs changed, and how, by retaining a fee, we were able to allocate our budgets in ways which redistributed the surplus to participants in need (rather than bolster conference centre profits). We then explore what these changing costs meant in terms of our attendance levels across career stages and geographical locations. Looking at whether our experiment resulted in increased support for online events, we examine the continued ambivalence felt for the virtual. Finally, while we largely explore the benefits of online options, our last section urges caution over assumptions that this move will result in a more sustainable academia, particularly given the intensifications surrounding high quality streaming video, and suggest that we treat current trends as ongoing experiments, rather than solutions. This paper explores a broadened sense of the costs of academic conferences, and how moving online opens up opportunities for forms of administrative activism that reimagine these events as community economies. [ABSTRACT FROM AUTHOR]