Enhancing Kurdistan's manufacturing companies' sustainable waste management: A norm activation approach to green accounting, CSR, and environmental auditing oversight.

The significance of accurate energy production prediction cannot be overstated, especially in the context of achieving carbon neutrality and balancing traditional and clean energy sources. Unlike conventional models with simplified assumptions or limited data inputs hindering energy usage optimization, waste reduction and efficient resource allocation, we introduced a novel structural equation modelling approach to eight manufacturing industries' sustainable waste management practices (SWMPs) in Iraq. This comprehensive analysis, conducted with Smart PLS software on 375 responses aims to enhance energy production predictions' accuracy and support sustainability goals contribute to achieving carbon neutrality goals and promote a balanced energy mix that supports sustainability and environmental stewardship. The findings reveal noteworthy insights: notably, chemical manufacturing companies exhibit a substantial advantage from green accounting practices, witnessing a 78.1 % and 45.8 % improvement in environmental auditing oversight and SWMPs, respectively, compared to other manufacturing sectors. Compared to conventional grey models, our model demonstrates that a 1-unit improvement in CSR enhances environmental auditing oversight effectiveness by 33.4 % and sustainable waste management by 56.9 % across industries. By leveraging these data-driven insights and innovative approaches, we can drive positive change towards a more sustainable and resilient energy future, collectively contributing to a more resilient, efficient, and sustainable energy ecosystem that benefits societies, economies, and the environment. The heightened accuracy of energy production prediction facilitated by our novel model empowers stakeholders at regional and global levels to make informed decisions, mitigate risks, support policy development, achieve sustainability goals, formulate effective policies and foster collaboration.

Bibliometric network mapping of farm accounting studies using a comprehensive dataset.

This data article aims to analyze the intellectual structure of farm accounting studies by examining bibliometric features. A dataset comprising 190 documents from the ISI Database within the farm accounting field was utilized. It delved into various aspects including the yearly publication and citation count concerning agricultural accounting, predominant research areas, keyword co-occurrence, bibliographic coupling among sources and documents, as well as co-citation patterns of referenced materials. Bibliometric network mapping techniques were employed for the analysis of the data. The analysis was conducted using VOSviewer, a scientific mapping analysis tool. The findings indicated a notable uptrend in publication and citation rates of agricultural accounting studies over the past decade. A significant portion of the dataset centered around agriculture and business economics. Key terms like ``biological assets,'' ``IAS 41,'' and ``fair value'' emerged as prominently used. The journal ``Custos e Agronegócio Online'' showed significant influence in terms of bibliographic coupling among sources.

Unveiling the adverse effects of artificial intelligence on financial decisions via the AI-IMPACT model.

There is considerable enthusiasm for the potential of artificial intelligence (AI) to improve financial well-being. Despite this enthusiasm, it is important to underscore AI's potential adverse effects on consumers' financial decisions. We introduce the AI-IMPACT model, a unifying theoretical framework for how AI can influence consumers' financial decisions. The model details how AI impacts the marketplace, affecting psychological processes and consumer traits core to financial decision-making (e.g., pain of payment, financial literacy). We use the AI-IMPACT model to illustrate one way AI can reduce financial well-being as its influence on the marketplace (e.g., facilitating biometric payment methods) decreases consumers' pain of payment, increasing spending. Lastly, we use the AI-IMPACT model to identify areas for future research at the intersection of AI and financial decision-making.

A decision methodology for site-level ecosystem accounting.

The United Nations System of Environmental-Economic Accounting Ecosystem Accounting (SEEA EA) framework is the international standard for ecosystem accounting. To date, application of SEEA EA has been predominantly at large scales, usually at landscape and national levels. However, many environmental management decisions are taken locally, in site-specific contexts. While the use of SEEA EA continues to develop at all scales, there is currently no widely endorsed methodology for employing SEEA EA at local scales, such as the site level. We present a methodology for developing site-level ecosystem accounts, describing the important decisions at each step of the process. We also provide two case studies that demonstrate the context-dependent nature of the decision-making process of ecosystem accounting at small scales. The two major challenges for site-level accounting are stakeholder engagement and data availability. As the use of SEEA EA continues to increase in policy and decision-making processes worldwide, there is a need for local-scale case studies that adapt this methodology across a broad range of contexts. Our case studies provide some of the first published examples of the application of SEEA EA at the site level and are intended to promote consistent implementation of ecosystem accounting across scales.

The impact of managers' attitudes towards environmental management accounting and green competitive advantage in Vietnam manufacturers.

Environmental management accounting (EMA) has positive support for providing information for green competitive advantage. Based on upper echelons theory (UET), this study examines the impact of upper managers' attitudes towards the environment regarding the choice of green production strategy, EMA implementation, and green competitive advantage (GCA). The moderating role of regulatory pressure (RP) in some relationships is also investigated. This study, conducted in Vietnam, features a data set of 234 medium and large manufacturers. The sample is restricted to medium and large firms only, as these possess sufficient financial resources to operate independent marketing and management accounting functions. Partial Least Squares Structural Equation Modeling (PLS-SEM) is used to test hypotheses. We found that it has a significant positive and direct effect of attitude towards the environment on choosing a green production strategy and the implementation of EMA, thereby achieving a green competitive advantage. We also find that regulatory pressures have a moderator role and are positive in some relationships. This study applied UET in the management accounting field and presented the significance of the compatibility between upper managers' attitudes towards the environment, strategies, and EMA implementation that would improve the long-term competitive advantage of the organizations.

Mapping quantity, composition, and embedded environmental impacts of post-consumer waste in the food service industry in China.

Understanding the current state of food waste is the basis for effective interventions. Based on field research conducted for 133 days, from March to August 2022, and for 25 days in November 2022, totaling 158 days, this study obtained first-hand data on the waste and consumption of 103 food items in ten categories at sample restaurants in different regions and city levels, and of different operation sizes in China. A total of 7759 consumers were part of this study, of whom 10 % them were children. The food delivered to a total of 2538 tables was counted as part of this study, and the average number of people per table in this study was three. The research objective was to measure the quantity, composition, and environmental impacts of post-consumer waste in the food service industry at the national level. It was discovered that: (1) Food waste generated by post-consumers in China totaled 7.57 Mt, or 43.98 g per capita per meal in 2022. (2) Fruit was the most important type of waste at 1.51 Mt. (3) Roots and tubers had the highest food waste rate (53 %). (4) The annual food waste resulted in a carbon footprint of 30.67 Mt CO2-eq, a nitrogen footprint of 393.94 million kilograms (Mkg N), a phosphorus footprint of 53.87 Mkg P, a water footprint of 17.09 million litres, and a land footprint of 4.36 million hectares (Mha).

Low-carbon development path based on carbon emission accounting and carbon emission performance evaluation: a case study of Chinese coal production enterprises.

Carbon emission accounting is the basic premise of effective carbon emission reduction and management. This study aimed to establish the carbon emission model and performance evaluation framework of coal mine production enterprises and clarify the low-carbon development path of enterprises. In this study, we took a typical coal production enterprise (K enterprise) in the Shanxi province of China as the research object. We also estimated the carbon emissions of the enterprise mainly according to the Chinese Carbon Emission Accounting Standard (GB/T 32151.11-2018). The triangular model was used to construct the carbon performance evaluation framework. On this basis, we suggested the enterprise's low-carbon development path. The results showed that (1) the carbon emission of K enterprise in 2021 was 36,875.38 tCO2eq; the carbon emission intensity of each ton of coal produced was 0.089 tCO2eq. The critical carbon emissions were electricity consumption and methane fugitive emissions during production. (2) The evaluation indicators for carbon emission performance revealed an imbalance in K enterprise's economic, energy, and environmental development in 2021. The work on energy saving and consumption reduction was relatively weak. (3) Countermeasures for low-carbon development, including a carbon emission ledger, were proposed based on carbon emission accounting and performance evaluation results. This study can help typical underground coal production enterprises in Shanxi province obtain more accurate carbon emission data, providing practical guidance and reference for the same underground coal production enterprises to improve the carbon emission control effect.

Bibliometric analysis of research hotspots and trends in the field of volatile organic compound (VOC) emission accounting.

Volatile organic compounds (VOCs) have been extensively studied because of their significant roles as precursors of atmospheric ozone and secondary organic aerosol pollution. The research aims to comprehend the current advancements in domestic and international VOC emission accounting. The study utilized the CiteSpace software to represent the pertinent material from Web of Science visually. The hot spots and future development trends of VOC emission calculation are analyzed from the perspectives of thesis subject words, cooperative relationships, co-citation relationships, journals, and core papers. According to the statistics, the approaches most often employed in VOC accounting between 2013 and 2023 are source analysis and emission factor method. Atmospheric environment is the journal with the most publications in the area. The Chinese Academy of Sciences and the University of Colorado System are prominent institutions in VOC emission accounting research, both domestically and internationally. The primary research focuses on the realm of VOC emission accounting clusters, which are "emission factor," "source analysis," "model," "air quality," and "health." A current trend in VOC emission accounting involves the construction of a VOC emission inventory using a novel model that combines emission factors and source analysis. This study reviews the progress made in calculating volatile organic compound (VOC) emissions over the past decade. It aims to provide researchers with a new perspective to promote the development of this field.

Double Counting of Emission Reductions Undermines the Credibility of Corporate Mitigation Claims.

Companies are increasingly relying on emission reductions attributable to their adoption of renewable electricity to achieve net-zero emission targets. However, there is a risk of double counting of emission reductions threatening the credibility of corporate climate actions due to defective accounting rules of GHG emissions related to electricity consumption and the overlap between different market-based instruments, including carbon credit markets, renewable power purchase agreements, and renewable energy certificates. Using data of 63 major Chinese companies in seven sectors, we quantitatively assess the risks of double counting related to corporate sourcing of renewables and their consequent influences on the alignment of corporate emission trajectories with the 1.5 °C goal of the Paris Agreement. Results show that 7.1% of the electricity consumed by sample companies in 2021 was from renewable energy procurement and deployment, with which they reported 8.27 Mt of CO2e emission reductions compared to the scenario with no renewable electricity consumption. However, emission reductions that could be double counted are predicted to be 0.9-1.3 times as many as emission reductions that companies will report during 2021-2030. After adjustment of the reported emissions that might be underestimated due to double counting, the overall emission trajectories of sample companies are no longer aligned with the 1.5 °C goal. Our findings suggest that it is urgently needed to improve the corporate carbon accounting rules and increase the transparency of corporate carbon disclosures.

The impact of external auditors with forensic accounting competencies on auditee firm performance.

The purpose of this study is to examine how the presence of auditors with forensic accounting skills impacts the financial performance of audited companies. Using a quantitative approach, this research employs linear regression analysis and examines a sample of 74 companies from the industrial and service sectors listed on the Amman Stock Exchange between 2012 and 2021. The findings reveal that external auditors with forensic accounting competencies have a positive impact on the financial performance of audited companies. The insights presented in this study could serve as valuable tools for improving the financial performance of companies in the service and industry sectors, highlighting the importance of supporting and promoting the skills and competencies of forensic accountants.

Evolution of blockchain accounting literature from the perspective of CiteSpace (2013-2023).

Against the backdrop of the Industrial Revolution 4.0, the advantages of blockchain technology in traceability, transparency, safety improvement, and efficiency improvement have made it possible to reduce the work of accounting personnel by 50 %, thus saving billions of dollars for global companies by combining this technology with accounting. However, the blockchain technology associated with accounting is in the experimental stage and has several problems to be solved including limited data processing capacity, information confidentiality, and regulatory difficulties. This innovation and progress in science and technology has provided more abundant, efficient, and professional technical support for the research of blockchain accounting documents. Among these advances, CiteSpace software has promoted the development of blockchain and accounting in the direction of visualization, comprehensiveness, security, and relevance. In this study, we used the knowledge map drawn by CiteSpace to search the core Blockchain Accounting database from 2013 to 2023 on the Web of Science (WoS). We obtained 1414 documents measured according to co-citation analysis, log-likelihood ratio (LLR) network clustering, co-occurrence keywords, and emergent time zone diagram method. We analyzed and summarized the important documents, research keywords, key research fields, and knowledge evolution related to "blockchain accounting" by network, literature integration, and popular research topics. We found that adopting blockchain technology in accounting information systems is expected to improve recordkeeping and reporting. Blockchain, as an innovative technology, provides a tamper-proof, traceable, and shareable platform for accounting information by using a distributed ledger system. By implementing blockchain, artificial intelligence can improve safety, transparency, and accuracy, and also may completely change the way we manage financial records. With its ability to improve overall efficiency and reduce errors, blockchain technology may change our familiar accounting methods. In addition, blockchain technology, intelligent contract, artificial intelligence, the Internet, information systems, and supply chain are the most important keywords, while blockchain technology, intelligent contract, and artificial intelligence are important components of blockchain accounting knowledge system. This research provided an important opportunity to advance the understanding of the crucial contribution of blockchain to the accounting field.

Based on knowledge capital value for disease cost accounting of diagnosis related groups.

Background: The National Health Commission and the other relevant departments in China have initiated testing of the Diagnosis Related Groups (DRGs) system in 30 pilot locations since 2019. In the process of DRG payment reform, accounting for the costs of diseases has become a highly challenging issue. The traditional method of disease accounting method overlooks the compensation for the knowledge capital value of medical personnel. Objective: The primary objective of this study is to analyze the cost accounting scheme of China's Diagnosis Related Groups (C-DRG), focusing on the value of knowledge capital. Methods: The study initially proposes a measurement index system for the value of knowledge-based capital, including the difficulty of disease treatment, labor intensity of disease treatment, risk of disease treatment, and operation/treatment time for diseases. The Analytic Hierarchy Process (AHP) is then utilized to weigh the features of medical workers' knowledge capital value. First, pairwise comparisons are conducted in this stage to develop a two-pair judgment matrix of the primary indicators. Second, the eigenvectors corresponding to the maximum eigenvalues of the matrix are calculated to generate the weight coefficient of each feature. The consistency test is carried out after this stage. An empirical analysis is conducted by collecting data, including the full costs of treating three types of diseases-hip replacement, acute simple appendicitis, and heart bypass surgery-from one public medical institution. Results: The empirical analysis examines whether this DRG costing accounting can address the issue of neglecting the value of medical workers' knowledge capital. The methods reconfigure the positive incentive mechanism, stimulate the endogenous motivation of the medical service system, foster independent changes in medical behavior, and achieve the goals of reasonable cost control. Conclusion: In the cost accounting system of C-DRG, the value of medical workers' knowledge capital is acknowledged. This acknowledgment not only boosts the enthusiasm and creativity of medical workers in optimizing and standardizing the diagnosis and treatment process but also improves the transparency and authenticity of DRG pricing. This is particularly evident in the optimization and standardization of the diagnosis and treatment processes within medical institutions and in monitoring inadequate medical practices within these institutions.

Promoting the Achievement of Carbon Neutrality Targets: Systematic Review of Research Dimensions and Measure Methods on Carbon Equity in China.

Carbon equity is the balance between carbon reduction responsibilities and development rights. The review of carbon equity in China can help it achieve carbon neutrality targets and provide valuable insights to other emerging countries. This study aimed to systematically sort, classify, compare, and prospect the research dimensions and measure methods for carbon equity. The research dimensions were first classified into intergenerational, regional, trade, and income carbon equity by literature analysis. Intergenerational carbon equity explores the balance of carbon emission rights among generations using integrated assessment models (IAM). Regional carbon equity analyzes the socioeconomic effect of regional carbon emission rights allocation by IAM or regional differences under a specific allocation assumption by the Theil index. Trade carbon equity studies the relationship between carbon emissions and economic benefit transfer embodied in inter-regional trade, which is more suitable for calculating by methods with comparable results, such as the optimized regional environmental inequality index. Income carbon equity investigates the carbon footprint heterogeneity among income groups by the carbon Gini coefficient. This paper further discusses potential research directions for each dimension. Notably, all research dimensions did not consider promoted strategies for carbon equity, which should be a priority for future studies.

Soil environmental carrying capacity and its spatial high-precision accounting framework.

Human activity intensity should be controlled within the carrying capacity of soil units, which is crucial for environmental sustainability. However, the existing assessment methods for soil environmental carrying capacity (SECC) rarely consider the relationship between human activity intensity and pollutant emissions, making it difficult to provide effective early warning of human activity intensity. Moreover, there is a lack of spatial high-precision accounting methods for SECC. This study first established a spatial soil environmental capacity (SEC) model based on the pollutant thresholds corresponding to the specific protection target. Next, a spatial net-input flux model was proposed based on soil pollutants' input/output fluxes. Then, the quantitative relationship between human activity intensity and pollutant emissions was established and further incorporated into the SECC model. Finally, the spatial high-precision accounting framework of SECC was proposed. The methodology was used to assess the SECC for the copper production capacity in a typical copper smelting area in China. The results showed that (i) the average SECs for Cu, Cd, Pb, Zn, As and Cr are 427.89, 16.84, 306.41, 376.8, 71.63, and 392.7 kg hm-2, respectively; (ii) heavy metal (HM) concentrations and land-use types jointly influence the spatial distribution pattern of SEC; (iii) atmospheric deposition is the dominant HM input pathway and the high net-input fluxes are mainly located in the southeast of the study area; (iv) with the current human activity intensity for 50 years, the average SECs for Cu, Cd, Pb, Zn, As and Cr are 202.31, 1.71, 20.9, 66.15, 36.73, and 3 kg hm-2, respectively; and (v) to maintain the protection target at the acceptable risk level within 50 years, the SECC for the increased copper production capacity is 1.53 × 106 t. This study provided an effective tool for early warning of human activity intensity.

Balancing the books of nature by accounting for ecosystem condition following ecological restoration.

Demand for ecological restoration of Earth's degraded ecosystems has increased significantly since the adoption of The Kunming-Montreal Global Biodiversity Framework in December 2022, with target 2 aiming to ensure that at least 30% of degraded ecosystems are under effective restoration by 2030. More recently, in December 2023, the Australian Parliament introduced the Nature Repair Act, which establishes a framework for the world's first legislated, national, voluntary biodiversity market. How can the effectiveness of these ambitious targets be measured? Natural Capital Accounting (NCA) provides a framework to measure changes in ecosystem condition that is applicable across ecosystems and potentially catalogue effects of restoration interventions to drive investment, improvement to practice, and ultimately, to better protect the Earth's ecosystems. However, the framework has not been tested in this context. In this progressive approach, we populated the leading global NCA framework with ecological data to quantify changes in ecosystem condition after restoration. In principle, NCA is fit for purpose, however, methodological refinements and ecological expertise are needed to unlock its full potential. These tweaks will facilitate adoption and standardisation of reporting as efforts ramp up to meet ambitious global restoration targets.

Data mining algorithm in the identification of accounting fraud by smart city information technology.

The identification of accounting fraud is an important measure to safeguard the interests of stakeholders and ensure the long-term development of the company. The current traditional methods for identifying accounting fraud rely on manual review and judgment, lacking objectivity and accuracy. In order to improve the accuracy of accounting fraud identification, improve identification efficiency and objectivity, this article combines smart city information technology to conduct in-depth research on data mining algorithms for accounting fraud identification. This article first provides a brief overview of smart cities and information technology, then introduces the basic theory of accounting fraud identification, and finally implements accounting fraud identification through k-means clustering mining algorithm. The data is divided into k clusters, and abnormal clusters are identified by checking the characteristics and attributes of each cluster. Compared with traditional rule-based and pattern based methods, this approach can more flexibly adapt to different types and forms of fraud, and can discover unknown patterns of fraud. In the experiment, this article used electronic data collection, analysis, and retrieval systems on the websites of the Shanghai Stock Exchange and Shenzhen Stock Exchange to collect 641 annual reports and financial characteristics from 62 listed companies that engaged in financial statement fraud and 84 companies that were not reported to have financial statement fraud from 2012 to 2021 as test samples. The results were tested and analyzed from several aspects, including the number of misjudgments, misjudgment rate, and ROC curve. The final test results show that compared to traditional accounting fraud identification methods, the comprehensive misjudgment rate of data mining algorithms based on smart cities has decreased by 3 %. The conclusion indicates that data mining algorithms used in smart city information technology to identify accounting fraud can help improve the accuracy of accounting fraud, improve audit objectivity and effectiveness.

Healthcare cost accounting in the Indian hospital sector.

Setting reimbursement rates in national insurance schemes requires robust cost data. Collecting provider generated cost accounting information is a potential mechanism for improving the cost evidence. To inform strategies for obtaining cost data to set reimbursement rates, this analysis aims to describe the role of cost accounting in public and private health sectors in India and describe the importance, perceived barriers, and facilitators to improving cost accounting systems. In-depth interviews (IDI) were conducted with 11 key informants. The interview tool guide was informed by a review of published and grey literature and government websites. The interviews were recorded for both audio and video and transcribed. A thematic coding framework was developed for the analysis. Multiple discussions were held to add, delete, classify, or merge the themes. The themes identified were: the status of cost accounting in the Indian hospital sector, legal and regulatory requirements for cost reporting, challenges to implementing cost accounting, and recommendations for improving cost reporting by healthcare providers. The findings indicate that the sector lacks maturity in cost accounting due to a lack of understanding of its benefits, limited capacity, and weak enforcement of cost reporting regulations. Providers recognize the value of cost analysis for investment decisions but have mixed opinions on the willingness to gather and report cost information, citing resource constraints and a lack of trust in payers. Additionally, heterogeneity among providers will require tailored approaches in developing cost accounting reporting frameworks and regulations. Healthcare cost accounting systems in India are rudimentary with a few exceptions, raising questions about how to source these data sustainably. Strengthening cost accounting systems will be contingent upon developing standardized formats that generate sufficient information for policymaking, are acceptable to private providers, and can be integrated with the existing data management systems.

Emotions before actions: When children see costs as causal.

Adults expect people to be biased by sunk costs, but young children do not. We tested between two accounts for why children overlook the sunk cost bias. On one account, children do not see sunk costs as causal. The other account posits that children see sunk costs as causal, but unlike adults, think future actions cannot make up for sunk costs. These accounts make opposing predictions about whether children should see sunk costs as affecting emotions. Across three experiments, 4-7-year-olds (total N = 320) and adults (total N = 429) saw stories about characters who collected items that were easy or difficult to obtain, and predicted characters' emotions and actions. At all ages, participants anticipated that characters would feel sadder about high-cost objects, but only adults predicted that characters would keep high-cost objects. Our findings show that children see incurred costs as causal, and that costs are integrated children's and adults' theory of emotions. Moreover, the findings suggest that developmental differences in sunk cost reasoning may rest in children's incomplete mental accounting. We also discuss children's reasoning about rational and irrational action.

Methodological guide for assessing the carbon footprint of external beam radiotherapy: A single-center study with quantified mitigation strategies.

Background and purposes: Data on the carbon footprint of external beam radiotherapy (EBRT) are scarce. Reliable and exhaustive data, including a detailed carbon inventory, are needed to determine effective mitigation strategies. Materials and methods: This study proposes a methodology for calculating the carbon footprint of EBRT and applies it to a single center. Mitigation strategies are derived from the carbon inventory, and their potential reductions are quantified whenever possible. Results: The average emission per treatment and fraction delivered was 489 kg CO2eq and 27 kg CO2eq, respectively. Patient transportation (43 %) and the construction and maintenance of linear accelerators (LINACs) and scanners (17 %) represented the most significant components. Electricity, the only energy source used, accounted for only 2 % of emissions.Derived mitigation strategies include a data deletion policy (reducing emissions in 30 years by 12.5 %), geographical appropriateness (-12.2 %), transportation mode appropriateness (-9.3 %), hypofractionation (-5.9 %), decrease in manufacturers' carbon footprint (-5.2 %), and an increase in machine durability (-3.5 %). Conclusion: Our findings indicate that a significant reduction in the carbon footprint of a radiotherapy unit can be achieved without compromising the quality of care.This study provides a methodology and a starting point for comparison and proposes and quantifies mitigation strategies, paving the way for others to follow.

Real-time task parameter selection method of accounting system based on multi-objective optimization and genetic algorithm.

The progress of the digital economy has promoted the enterprise accounting system. To accelerate the update and evolution of accounting systems, we propose a parameter selection method based on multi-objective optimization and genetic algorithm. Firstly, this article proposes an accounting feature extraction method based on multimodal information embedding. The dual-branch structure and feature pyramid network are used to realize the feature extraction of the information involved in accounting. Then, this article proposes a multi-objective parameter selection method based on a parallel genetic algorithm. By embedding a genetic algorithm in the process of dual-branch model training, the model's ability to sense accounting information is improved. Finally, using the above two methods, an accounting system evaluation method upon recurrent Transformer is proposed to improve the financial situation of enterprises. Our experiments have proven that our approach attains a remarkable performance with an 87.6% F-value, 83.5% mAP value, and 83.4% accuracy. These results position our method at an advanced level globally, showcasing its capability to enhance accounting systems.