Integration of an electronic hand hygiene auditing system with electronic health records using machine learning to predict hospital-acquired infection in a healthcare setting.

BACKGROUND: Hospital-acquired infections (HAIs) increase morbidity, mortality, and healthcare costs. Effective hand hygiene (HH) is crucial for prevention, but achieving high compliance remains challenge. This study explores using machine learning to integrate an electronic HH auditing system with electronic health records to predict HAIs. METHODS: A retrospective cohort study was conducted at a Brazilian hospital during 2017-2020. HH compliance was recorded electronically, and patient data were collected from electronic health records. The primary outcomes were HAIs per CDC/NHSN surveillance definitions. Machine learning algorithms, balanced with Random Over Sampling Examples (ROSE), were utilized for predictive modeling, including generalized linear models (GLM); generalized additive models for location, scale, and shape (GAMLSS); random forest; support vector machine; and extreme gradient boosting (XGboost). RESULTS: 125 of 6,253 patients (2%) developed HAIs and 920,489 HH opportunities (49.3% compliance) were analyzed. A direct correlation between HH compliance and HAIs was observed. The GLM algorithm with ROSE demonstrated superior performance, with 84.2% sensitivity, 82.9% specificity, and a 93% AUC. CONCLUSIONS: Integrating electronic HH auditing systems with electronic health records and using machine learning models can enhance infection control surveillance and predict patient outcomes. Further research is needed to validate these findings and integrate them into clinical practice.

An improved identity-based public audit protocol for cloud storage.

With the rapid development of informatization, a vast amount of data is continuously generated and accumulated, leading to the emergence of cloud storage services. However, data stored in the cloud is beyond the control of users, posing various security risks. Cloud data auditing technology enables the inspection of data integrity in the cloud without the necessity of data downloading. Among these, public auditing schemes have experienced rapid development due to their ability to avoid additional user auditing expenses. However, malicious third-party auditors can compromise data privacy. This paper proposes an improved identity-based cloud auditing scheme that can resist malicious auditors. This scheme is also constructed on an identity-based public auditing scheme using blockchain to prevent malicious auditing. We found the scheme is not secure because a malicious cloud server can forge authentication tags for outsourced data blocks, while our scheme has not these security flaws. Through security proofs and performance analysis, we further demonstrate that our scheme is secure and efficient. Additionally, our scheme has typical application scenarios.

Special supplement issue on quality assurance and enrichment of biological and biomedical ontologies and terminologies.

Ontologies and terminologies serve as the backbone of knowledge representation in biomedical domains, facilitating data integration, interoperability, and semantic understanding across diverse applications. However, the quality assurance and enrichment of these resources remain an ongoing challenge due to the dynamic nature of biomedical knowledge. In this editorial, we provide an introductory summary of seven articles included in this special supplement issue for quality assurance and enrichment of biological and biomedical ontologies and terminologies. These articles span a spectrum of topics, such as development of automated quality assessment frameworks for Resource Description Framework (RDF) resources, identification of missing concepts in SNOMED CT through logical definitions, and developing a COVID interface terminology to enable automatic annotations of COVID-19 related Electronic Health Records (EHRs). Collectively, these contributions underscore the ongoing efforts to improve the accuracy, consistency, and interoperability of biomedical ontologies and terminologies, thus advancing their pivotal role in healthcare and biomedical research.

A study on awareness and perception about perinatal death auditing among health care workers in two districts of Karnataka State, India.

Background: To start perinatal death auditing, doctors should have good knowledge about it. Objectives: To know the awareness and perceptions of doctors about different aspects of perinatal death auditing like 1) different types of contributors; 2) high-risk approach; 3) consequences; 4) documentary requirements; and 5) existing system of mortality meeting/child death reviews. Methodology: The perinatal death auditing project was implemented in two districts of Karnataka state. As a part of the pre-intervention survey, awareness and perceptions of doctors and a few health care administrators were explored. They were requested to participate in the study. Those who consented were approached in their hospitals and interviewed. Trained medical social workers conducted the interviews. Awareness was scored from 0 to 3 with 0 being no knowledge and 3 being good knowledge. Perceptions were scored from 0 to 3 with 0 being no negative perceptions and 3 being fear of legal consequences. The responses were documented, scored, and described. Results: Though 22 doctors were eligible, only 16 consented to participate in the study. Knowledge of doctors about different contributors was inadequate. They were apprehensive about legal consequences. They knew that documentation could protect them and be useful in a court of law. They were not clear about the conduct of mortality meeting/existing system of child death reviews. Conclusion: Knowledge was inadequate. They were apprehensive about legal consequences. Training of doctors and allaying apprehensions are required for starting perinatal death auditing.

Towards algorithm auditing: managing legal, ethical and technological risks of AI, ML and associated algorithms.

-Business reliance on algorithms is becoming ubiquitous, and companies are increasingly concerned about their algorithms causing major financial or reputational damage. High-profile cases include Google's AI algorithm for photo classification mistakenly labelling a black couple as gorillas in 2015 (Gebru 2020 In The Oxford handbook of ethics of AI, pp. 251-269), Microsoft's AI chatbot Tay that spread racist, sexist and antisemitic speech on Twitter (now X) (Wolf et al. 2017 ACM Sigcas Comput. Soc. 47, 54-64 (doi:10.1145/3144592.3144598)), and Amazon's AI recruiting tool being scrapped after showing bias against women. In response, governments are legislating and imposing bans, regulators fining companies and the judiciary discussing potentially making algorithms artificial 'persons' in law. As with financial audits, governments, business and society will require algorithm audits; formal assurance that algorithms are legal, ethical and safe. A new industry is envisaged: Auditing and Assurance of Algorithms (cf. data privacy), with the remit to professionalize and industrialize AI, ML and associated algorithms. The stakeholders range from those working on policy/regulation to industry practitioners and developers. We also anticipate the nature and scope of the auditing levels and framework presented will inform those interested in systems of governance and compliance with regulation/standards. Our goal in this article is to survey the key areas necessary to perform auditing and assurance and instigate the debate in this novel area of research and practice.

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.

Is auditor financial decision-making affected by prior audit report information? A behavioral approach.

The role of financial experts is to provide their professional judgments with an opinion included in the financial reports after reviewing an entity's financial information, following a specific audit process. We investigate whether confirmation bias (through prior audit opinions) occurs among auditors during the audit process and decision-making, and whether experience mitigates this effect. A total of 175 non-experienced auditors run a 2x4 between-subjects experiment (experiment 1) studying how financial information (IV with two levels: negative and neutral/positive) and previous audit report (IV with four levels: absence, negative, moderately negative, and positive) might influence the issuance of the subsequent decision-making (DV). In addition, a total of 32 junior level 1 auditors (less than one year of experience), 31 junior level 2 auditors (up to 3 years of experience) and 20 senior auditors (more than 3 years of experience) run a 2 × 4 × 3 between-subjects experiment (experiment 2) analyzing if experience (IV with three levels of experience: less than one-year, between one and three years, more than three years) mitigates this effect (experiment 2). Results confirm that the previous-year audit report affect auditors' current assessment, showing that positive and negative prior opinions persuade auditors when suggesting the next one. This finding is relevant as auditors' opinions could be conditioned by prior opinions instead of their own expertise. Our evidence also suggests that professional experience mitigates this influence on auditors' assessments. Consequently, this study has relevant implications for partners, audit professionals and audit firm recruiters. A general implication is that auditor training courses should reinforce the auditor's own expertise and criteria based on the deep analysis of financial and economic data rather than on the work of previous auditors.

Representativeness and face-ism: Gender bias in image search.

Implicit and explicit gender biases in media representations of individuals have long existed. Women are less likely to be represented in gender-neutral media content (representation bias), and their face-to-body ratio in images is often lower (face-ism bias). In this article, we look at representativeness and face-ism in search engine image results. We systematically queried four search engines (Google, Bing, Baidu, Yandex) from three locations, using two browsers and in two waves, with gender-neutral (person, intelligent person) and gendered (woman, intelligent woman, man, intelligent man) terminology, accessing the top 100 image results. We employed automatic identification for the individual's gender expression (female/male) and the calculation of the face-to-body ratio of individuals depicted. We find that, as in other forms of media, search engine images perpetuate biases to the detriment of women, confirming the existence of the representation and face-ism biases. In-depth algorithmic debiasing with a specific focus on gender bias is overdue.

Effect of surgical volume on short-term outcomes of cytoreductive surgery for advanced-stage ovarian cancer: A population-based study from the Dutch Gynecological Oncology Audit.

OBJECTIVE: Despite lacking clinical data, the Dutch government is considering increasing the minimum annual surgical volume per center from twenty to fifty cytoreductive surgeries (CRS) for advanced-stage ovarian cancer (OC). This study aims to evaluate whether this increase is warranted. METHODS: This population-based study included all CRS for FIGO-stage IIB-IVB OC registered in eighteen Dutch hospitals between 2019 and 2022. Short-term outcomes included result of CRS, length of stay, severe complications, 30-day mortality, time to adjuvant chemotherapy, and textbook outcome. Patients were stratified by annual volume: low-volume (nine hospitals, <25), medium-volume (four hospitals, 29-37), and high-volume (five hospitals, 54-84). Descriptive statistics and multilevel logistic regressions were used to assess the (case-mix adjusted) associations of surgical volume and outcomes. RESULTS: A total of 1646 interval CRS (iCRS) and 789 primary CRS (pCRS) were included. No associations were found between surgical volume and different outcomes in the iCRS cohort. In the pCRS cohort, high-volume was associated with increased complete CRS rates (aOR 1.9, 95%-CI 1.2-3.1, p = 0.010). Furthermore, high-volume was associated with increased severe complication rates (aOR 2.3, 1.1-4.6, 95%-CI 1.3-4.2, p = 0.022) and prolonged length of stay (aOR 2.3, 95%-CI 1.3-4.2, p = 0.005). 30-day mortality, time to adjuvant chemotherapy, and textbook outcome were not associated with surgical volume in the pCRS cohort. Subgroup analyses (FIGO-stage IIIC-IVB) showed similar results. Various case-mix factors significantly impacted outcomes, warranting case-mix adjustment. CONCLUSIONS: Our analyses do not support further centralization of iCRS for advanced-stage OC. High-volume was associated with higher complete pCRS, suggesting either a more accurate selection in these hospitals or a more aggressive approach. The higher completeness rates were at the expense of higher severe complications and prolonged admissions.

Scaling up search engine audits: Practical insights for algorithm auditing.

Algorithm audits have increased in recent years due to a growing need to independently assess the performance of automatically curated services that process, filter and rank the large and dynamic amount of information available on the Internet. Among several methodologies to perform such audits, virtual agents stand out because they offer the ability to perform systematic experiments, simulating human behaviour without the associated costs of recruiting participants. Motivated by the importance of research transparency and replicability of results, this article focuses on the challenges of such an approach. It provides methodological details, recommendations, lessons learned and limitations based on our experience of setting up experiments for eight search engines (including main, news, image and video sections) with hundreds of virtual agents placed in different regions. We demonstrate the successful performance of our research infrastructure across multiple data collections, with diverse experimental designs, and point to different changes and strategies that improve the quality of the method. We conclude that virtual agents are a promising venue for monitoring the performance of algorithms across long periods of time, and we hope that this article can serve as a basis for further research in this area.

Identity-based controlled delegated outsourcing data integrity auditing scheme.

With the continuous development of cloud computing, the application of cloud storage has become more and more popular. To ensure the integrity and availability of cloud data, scholars have proposed several cloud data auditing schemes. Still, most need help with outsourced data integrity, controlled outsourcing, and source file auditing. Therefore, we propose a controlled delegation outsourcing data integrity auditing scheme based on the identity-based encryption model. Our proposed scheme allows users to specify a dedicated agent to assist in uploading data to the cloud. These authorized proxies use recognizable identities for authentication and authorization, thus avoiding the need for cumbersome certificate management in a secure distributed computing system. While solving the above problems, our scheme adopts a bucket-based red-black tree structure to efficiently realize the dynamic updating of data, which can complete the updating of data and rebalancing of structural updates constantly and realize the high efficiency of data operations. We define the security model of the scheme in detail and prove the scheme's security under the difficult problem assumption. In the performance analysis section, the proposed scheme is analyzed experimentally in comparison with other schemes, and the results show that the proposed scheme is efficient and secure.

Outcomes of liver surgery: A decade of mandatory nationwide auditing in the Netherlands.

BACKGROUND: In 2013, the nationwide Dutch Hepato Biliary Audit (DHBA) was initiated. The aim of this study was to evaluate changes in indications for and outcomes of liver surgery in the last decade. METHODS: This nationwide study included all patients who underwent liver surgery for four indications, including colorectal liver metastases (CRLM), hepatocellular carcinoma (HCC), and intrahepatic- and perihilar cholangiocarcinoma (iCCA - pCCA) between 2014 and 2022. Trends in postoperative outcomes were evaluated separately for each indication using multilevel multivariable logistic regression analyses. RESULTS: This study included 8057 procedures for CRLM, 838 for HCC, 290 for iCCA, and 300 for pCCA. Over time, these patients had higher risk profiles (more ASA-III patients and more comorbidities). Adjusted mortality decreased over time for CRLM, HCC and iCCA, respectively aOR 0.83, 95%CI 0.75-0.92, P < 0.001; aOR 0.86, 95%CI 0.75-0.99, P = 0.045; aOR 0.40, 95%CI 0.20-0.73, P < 0.001. Failure to rescue (FTR) also decreased for these groups, respectively aOR 0.84, 95%CI 0.76-0.93, P = 0.001; aOR 0.81, 95%CI 0.68-0.97, P = 0.024; aOR 0.29, 95%CI 0.08-0.84, P = 0.021). For iCCA severe complications (aOR 0.65 95%CI 0.43-0.99, P = 0.043) also decreased. No significant outcome differences were observed in pCCA. The number of centres performing liver resections decreased from 26 to 22 between 2014 and 2022, while median annual volumes did not change (40-49, P = 0.66). CONCLUSION: Over time, postoperative mortality and FTR decreased after liver surgery, despite treating higher-risk patients. The DHBA continues its focus on providing feedback and benchmark results to further enhance outcomes.

You can go your own way: Conducting a signage audit in a health library.

BACKGROUND: The Library and Knowledge Services at the Royal Berkshire NHS Foundation Trust has been at its current location since 1975 and, in September 2020, it was deemed necessary to examine the library environment and assess the signage. OBJECTIVES: The main objective was to conduct a methodical assessment of all the current library signage in order to improve the library environment, wayfinding, and clarity. METHODS: A methodical signage audit was conducted by library staff, photographing signs, assigning them to categories, and creating a signage map. RESULTS: In total, 58 different signs were counted in the library. Of these, 22 signs were deemed satisfactory, 4 should be moved to a more appropriate location, 15 should be updated with a branded design and better messaging, and 17 were recommended for removal. DISCUSSION: The results demonstrated that there were three main areas where signage could be improved: reducing the quantity of signs, updating the language and design, and the use of ad hoc signage. CONCLUSION: By assessing the signs and assigning categories and types, areas were identified as problem points that were impacting the library user's experience and small changes were implemented to improve the library environment.

Advanced exploitation of unmerged reflection data during processing and refinement with autoPROC and BUSTER.

The validation of structural models obtained by macromolecular X-ray crystallography against experimental diffraction data, whether before deposition into the PDB or after, is typically carried out exclusively against the merged data that are eventually archived along with the atomic coordinates. It is shown here that the availability of unmerged reflection data enables valuable additional analyses to be performed that yield improvements in the final models, and tools are presented to implement them, together with examples of the results to which they give access. The first example is the automatic identification and removal of image ranges affected by loss of crystal centering or by excessive decay of the diffraction pattern as a result of radiation damage. The second example is the `reflection-auditing' process, whereby individual merged data items showing especially poor agreement with model predictions during refinement are investigated thanks to the specific metadata (such as image number and detector position) that are available for the corresponding unmerged data, potentially revealing previously undiagnosed instrumental, experimental or processing problems. The third example is the calculation of so-called F(early) - F(late) maps from carefully selected subsets of unmerged amplitude data, which can not only highlight the location and extent of radiation damage but can also provide guidance towards suitable fine-grained parametrizations to model the localized effects of such damage.

Compiling and applying atmospheric resource balance sheet in off-office auditing of leading cadres in China.

Atmospheric resources provide important support for human economic and social systems through their unique ecosystem service functions. Implementing the off-office auditing of atmospheric resources for leading cadres holds great strategic significance for fundamentally solve air pollution problems. Exploring the compilation of an atmospheric resource balance sheet is a necessary pre-step in the implementation of outgoing audits. In this paper, atmospheric resources are innovatively divided into atmospheric capacity resources and atmospheric quality resources from the perspective of the ecosystem service functions of atmospheric resources. The value of atmospheric resource assets is calculated by combining the environmental capacity value method and the environmental loss evaluation method. This study evaluates atmospheric ecological achievements based on the atmospheric resources balance sheet and related accounts as the data carrier and opens up the key "blocking point" of the off-office auditing of atmospheric resources. This paper takes Anhui Province as an example and applies the accounting results of the prepared atmospheric resource balance sheet to evaluate the atmospheric resource ecological achievements of leading cadres from 2016 to 2020. The results clearly reflect the ecological achievements and shortcomings of local leading cadres during their tenure.

A Review of IoT Firmware Vulnerabilities and Auditing Techniques.

In recent years, the Internet of Things (IoT) paradigm has been widely applied across a variety of industrial and consumer areas to facilitate greater automation and increase productivity. Higher dependability on connected devices led to a growing range of cyber security threats targeting IoT-enabled platforms, specifically device firmware vulnerabilities, often overlooked during development and deployment. A comprehensive security strategy aiming to mitigate IoT firmware vulnerabilities would entail auditing the IoT device firmware environment, from software components, storage, and configuration, to delivery, maintenance, and updating, as well as understanding the efficacy of tools and techniques available for this purpose. To this effect, this paper reviews the state-of-the-art technology in IoT firmware vulnerability assessment from a holistic perspective. To help with the process, the IoT ecosystem is divided into eight categories: system properties, access controls, hardware and software re-use, network interfacing, image management, user awareness, regulatory compliance, and adversarial vectors. Following the review of individual areas, the paper further investigates the efficiency and scalability of auditing techniques for detecting firmware vulnerabilities. Beyond the technical aspects, state-of-the-art IoT firmware architectures and respective evaluation platforms are also reviewed according to their technical, regulatory, and standardization challenges. The discussion is accompanied also by a review of the existing auditing tools, the vulnerabilities addressed, the analysis method used, and their abilities to scale and detect unknown attacks. The review also proposes a taxonomy of vulnerabilities and maps them with their exploitation vectors and with the auditing tools that could help in identifying them. Given the current interest in analysis automation, the paper explores the feasibility and impact of evolving machine learning and blockchain applications in securing IoT firmware. The paper concludes with a summary of ongoing and future research challenges in IoT firmware to facilitate and support secure IoT development.

Multi-replicas integrity checking scheme with supporting probability audit for cloud-based IoT.

Nowadays, more people are choosing to use cloud storage services to save space and reduce costs. To enhance the durability and persistence, users opt to store important data in the form of multiple copies on cloud servers. However, outsourcing data in the cloud means that it is not directly under the control of users, raising concerns about security and integrity. Recent research has found that most existing multicopy integrity verification schemes can correctly perform integrity verification even when multiple copies are stored on the same Cloud Service Provider (CSP), which clearly deviates from the initial intention of users wanting to store files on multiple CSPs. With these considerations in mind, this paper proposes a scheme for synchronizing the integrity verification of copies, specifically focusing on strongly privacy Internet of Things (IoT) electronic health record (EHR) data. First, the paper addresses the issues present in existing multicopy integrity verification schemes. The scheme incorporates the entity Cloud Service Manager (CSM) to assist in the model construction, and each replica file is accompanied with its corresponding homomorphic verification tag. To handle scenarios where replica files stored on multiple CSPs cannot provide audit proof on time due to objective reasons, the paper introduces a novel approach called probability audit. By incorporating a probability audit, the scheme ensures that replica files are indeed stored on different CSPs and guarantees the normal execution of the public auditing phase. The scheme utilizes identity-based encryption (IBE) for the detailed design, avoiding the additional overhead caused by dealing with complex certificate issues. The proposed scheme can withstand forgery attack, replace attack, and replay attack, demonstrating strong security. The performance analysis demonstrates the feasibility and effectiveness of the scheme.

Soil carbon-nutrient cycling, energetics, and carbon footprint in calcareous soils with adoption of long-term conservation tillage practices and cropping systems diversification.

Calcareous soils, comprising vast areas in northern and eastern parts of India, are characterized by low soil organic carbon (SOC) with high free CaCO3 that results in low nutrient bioavailability with poor soil structure. Improvement of this soil can be achieved with conservation tillage with residue retention coupled with diversification of cropping system including legumes, and oilseeds in the system. Concerning all these, a long-term experiment was carried out in the calcareous soils having low organic carbon and high free CaCO3 (∼33 %) with varied tillage practices, viz. permanent bed with residue (PB), zero tillage with residue (ZT), and conventional tillage without residue (CT); and cropping systems viz. maize-wheat-greengram (MWGg), rice-maize (RM), and maize-mustard-greengram (MMuGg) during 2015-2021. From this study, it was observed that PB and ZT resulted in ∼25-30 % increment in SOC compared to the initial SOC, while CT showed a 4 % decrease in the SOC. Conservation tillage practices also resulted in better soil aggregation and favourable bulk density of the soil. Furthermore, PB and ZT practice exhibited 10-13 %; 15-18 %; 11-15 %; 40-60 %, 20-36 %, and 23-45 % increments in the soil available N, P, K, soil microbial biomass carbon, dehydrogenase activity, and urease activity, respectively over those under CT. Crop diversification with the inclusion of legume and oilseed crops (MMuGg, and MWGg) over cereal-dominated RM systems resulted in better soil health. Maize equivalent yield and energy use efficiency (%) were also found to be the maximum under PB, and ZT, in combination with the MMuGg system. ZT and PB also reduced the carbon footprint by 465 and 822 %, respectively over CT by elevating SOC sequestration. Hence, conservation tillage practices with residue retention coupled with diversification in maize-based cropping systems with mustard and greengram can improve soil health, system productivity, and energetics, and reduce the carbon footprint in calcareous soils.

Have government environmental auditing contributed to the green transformation of Chinese cities?

Faced with growing ecological problems, governments around the world are increasingly focusing on improving ecology and the environment. The topic of urban green transformation has attracted a great deal of research. However, not much of it has focused on the effectiveness of government environmental auditing, especially from the perspective of its role in sustainable governance. This article takes 285 cities in China from 2009 to 2020 as the research scale. It innovatively measures government environmental audits with dual indicators and uses System Gaussian Mixture Model (SGMM) to estimate that government environmental audits significantly promote urban green transformation, and the impact of "whether to implement government environmental auditing" is greater than the "intensity of government environmental auditing". The results show that government environmental audit intensity has a stronger impact on urban green transformation in eastern cities. In contrast, environmental audit coverage has a stronger impact in western cities. Moreover, the effect of government environmental auditing on green transformation is more significant in small and medium-sized cities and key environmental protection cities than in large cities and non-key environmental protection cities, respectively. Government environmental auditing could facilitate urban green transformation by restraining local government behavior, forcing green technology innovation, and promoting industrial structural upgrading. In addition, the intensity of government environmental auditing can better act on green transformation through the fore-mentioned mechanisms. It can play a crucial role in green technology innovation.

Unpacking the modeling process for energy policy making.

This article explores how the modeling of energy systems may lead to an undue closure of alternatives by generating an excess of certainty around some of the possible policy options. We retrospectively exemplify the problem with the case of the International Institute for Applied Systems Analysis (IIASA) global modeling in the 1980s. We discuss different methodologies for quality assessment that may help mitigate this issue, which include Numeral Unit Spread Assessment Pedigree (NUSAP), diagnostic diagrams, and sensitivity auditing (SAUD). We illustrate the potential of these reflexive modeling practices in energy policy-making with three additional cases: (i) the case of the energy system modeling environment (ESME) for the creation of UK energy policy; (ii) the negative emission technologies (NETs) uptake in integrated assessment models (IAMs); and (iii) the ecological footprint indicator. We encourage modelers to adopt these approaches to achieve more robust, defensible, and inclusive modeling activities in the field of energy research.