Search Results (176)

This study examines the visitor-demand impact of a government-funded payment for ecosystem services policy applied to cultural heritage sites. In May 2023, South Korea abolished cultural heritage visitation fees at national-park temples. This policy shift replaced a decades-long user-pay model with government compensation to temples, effectively transitioning to a government-funded PES framework. The reform aimed to resolve protracted social conflict over fee collection at trailheads serving both religious and recreational visitors. Utilizing monthly visitation data spanning January 2020 to December 2024 (N = 960 site-months), we employed a difference-in-differences design with propensity score matching to compare eight treated temple sites against eight matched controls. The results indicate that fee abolition produced a statistically significant average increase of approximately 14,229 monthly visitors per treated site. The results proved robust across five distinct model specifications, and event-study analysis confirmed that the parallel trends assumption was satisfied. These findings provide quasi-experimental evidence that removing price barriers at heritage sites substantially increases observed visitor demand, a necessary condition for enhanced public accessibility to cultural ecosystem services. Whether this demand increase translates into broader welfare gains, such as regional economic spillovers, improved visitor well-being, or long-term fiscal sustainability of the subsidy, remains an important question for future research. Full article

This study examines whether stricter enforcement of mandatory social insurance contributions affects corporate income tax behavior in China. In the Chinese institutional context, mandatory social insurance refers to payroll-based employer and employee contributions to five statutory programs: basic pension insurance, basic medical insurance, work-injury insurance, unemployment insurance, and maternity insurance. These programs are directly related to social sustainability because they finance old-age income security, medical protection, workplace injury compensation, unemployment support, maternity protection, and labor-market stability. Using China’s 2018 social insurance collection reform as a quasi-natural experiment, we analyze A-share listed companies from 2014 to 2024 through a difference-in-differences design based on differential exposure between private firms and state-owned enterprises. To assess the reliability of the identification strategy, we employ firm and year fixed effects, event-study analysis, placebo tests, alternative measures of tax avoidance, and propensity score matching difference-in-differences robustness checks. The findings show a tax-fee seesaw effect: private firms subject to extensive regulatory scrutiny respond to more rigorous enforcement of social insurance contributions by increasing corporate income tax avoidance. Analysis of the mechanisms shows that the Whited-Wu index of financial constraints partially explains this phenomenon. The effect is more pronounced in firms with higher labor costs and greater administrative expense intensity, indicating that the increased response is driven by labor cost exposure and organizational discretion. By contrast, the effect is weaker among firms audited by the Big Four accounting networks—Deloitte, PricewaterhouseCoopers, Ernst & Young, and KPMG—indicating that high-quality external audits constrain aggressive tax planning. Regionally, the effect is most pronounced in eastern China, where markets, labor costs, and tax-planning services are more developed. The findings contribute to the sustainable development literature by demonstrating that reforms designed to strengthen social insurance sustainability can unintentionally weaken tax compliance if payroll contributions, tax administration, and corporate financial pressures are not coordinated. The study highlights the importance of integrated fiscal governance for achieving socially sustainable and fiscally balanced development. Full article

The student movement of 2015, dubbed “#FeesMustFall”, was a pivotal moment in South Africa, which saw students demonstrating against for tuition fees in institutions of higher education. It was the country’s most significant post-apartheid student movement, in which Twitter/X played an important role, serving as a space for information sharing. Although the #FeesMustFall movement has been widely studied, there is still limited testing of the emotional discourse and the perceived consequences. This study sought to fill that gap by computing sentiment analysis of Twitter/X from 2015–2016 and reveal how emotional stories influenced mobilisation and public opinion on higher education reform in South Africa. A Lexicon-based (Natural Language Processing) sentiment analysis approach was utilised to assess people’s sentimentsand thefindings unveiled the role of emotions in shaping and mobilising youths, leading to the protest. For instance, 51% of the analysed tweets expressed support for the movement’s goals. These tweets reflected a strong conviction that reducing student fees is essential for improving access to higher education and addressing social inequality, particularly access to higher education. In addition, 31% of the tweets exhibited criticism of the protest. This criticism or negativity may stem from concerns about the destruction of university property during the protests, as well as the feasibility of implementing proposed changes. Only 18% of the tweets presented neutral sentiments, i.e., neither a strongly positive nor a strongly negative outcome. These tweets expressed views that were not opinionated on the demonstrations. Overall, it can be noted that the emotional dimensions of social activism in the digital age highlight the significance of emotional discourse in shaping the impact of student dissatisfaction with the higher education system in South Africa. Full article

Natural disasters are devastating occurrences that have a major influence on the well-being of numerous individuals on a global scale. The primary goal of this study is to facilitate the rapid, transparent, and safe delivery of various aid such as food and clothing to people in disaster areas. For this purpose, a system has been established using blockchain technology in cooperation with institutions and humanitarian organizations. This system is designed to be accountable and reliable; it will supervise all processes from the source of aid materials to their distribution while protecting the personal information of disaster victims. The assistance process is improved using Smart Contracts in order to provide fast, effective, and coordinated assistance. Unlike existing humanitarian frameworks that rely on permissionless networks such as Bitcoin or Ethereum, this study proposes Hyperledger Fabric to ensure beneficiary privacy and eliminate per-transaction fees for end-users, thereby offering a more sustainable economic model for high-frequency aid distribution compared to public blockchains. The proposed system (Chainguard) addresses the ’efficiency gap’ in the current literature JSON Web Token (JWT)-based authentication layer. The results showed that Chainguard achieves a stable throughput of ~180 TPS with an end-to-end latency of less than 1.5 s, outperforming traditional heavy-cryptography models in terms of scalability and resource efficiency during real-time disaster response. Full article

The secure management of classified documents containing sensitive information is critical for governments, military organizations, and the industry. Traditional data loss prevention (DLP) systems lack robustness against insider threats, particularly regarding access log integrity and tamper-proof auditing. To address log security, the previous literature has proposed multiple solutions, including private and hybrid blockchain models (e.g., Ethereum + MultiChain) to ensure audit trail integrity. However, hybrid architectures often face challenges such as unpredictable transaction costs (gas fees) and potential privacy risks when scaled for enterprise DLP logs. Conversely, private architectures may require higher resources, potentially causing bottlenecks on endpoints. In this paper, we propose an optimized Blockchain-Based Classified Document Management System (B2CDMS) utilizing a permissioned architecture. Our work demonstrates the challenges, advantages, and weak points of current solutions. We optimized a permissioned blockchain (BC) (Hyperledger Fabric v2.5) with an External Chaincode Builder using the Chaincode-as-a-Service (CCaaS) pattern. We compared our proposed private architecture with a hybrid architecture (Ethereum + MultiChain) and a public solution (Ethereum). We conducted a comprehensive analysis using pseudo Trellix ePolicy Orchestrator (ePO) Data Loss Prevention (DLP) logs. Experimental results on an Apple Silicon M4 (Apple Inc., Cupertino, CA, USA) testbed show that the proposed architecture achieves a throughput of 845.8 Transactions Per Second (TPS) with a sub-second latency of 55 ms, aiming to eliminate the bottlenecks of public blockchains. Furthermore, the system introduces a privacy-preserving hashing mechanism (i.e., committing only deterministic Secure Hash Algorithm 256-bit (SHA-256) digests to the immutable ledger while keeping the actual sensitive Personally Identifiable Information (PII) strictly in off-chain databases) compliant with General Data Protection Regulation (GDPR). It ensures that classified document metadata remains immutable and secure against rogue access benefiting from admin privileges. This study concludes that permissioned blockchain architectures offer a scalable and resource-efficient solution for forensic evidence preservation throughout the classified document lifecycle. Full article

In Taiwan, some cases charge high diagnostic fees based merely on manual visual inspection or other simple checks, which has severely undermined public trust and delayed judicial resolutions, forcing courts to repeatedly appoint alternative evaluators and prolonging dispute timelines. Based on convenient sampling under a 95% confidence level with a 10% margin of error and a 10–90% category proportion, this study analyzes 83 leakage identification cases collected through convenience sampling, covering diverse building types, leakage causes, and detection techniques such as infrared imaging, borescopes, and moisture meters. A clustering-based pricing framework was applied to classify cases by inspection methods and leakage causes and to link them with cost intervals. After rigorous filtering, cost categorization, one-hot encoding, and normalization, the model revealed three distinct cost groups and achieved an overall classification accuracy of 86.75%, with particularly high precision in the medium-cost range. The findings confirm that advanced methods (e.g., borescopes, high-pressure cleaning) correspond to higher fees, while simpler approaches (e.g., infrared imaging) remain in lower cost brackets. This framework supports transparent and standardized fee estimation, addresses long-standing pricing controversies, and enhances consumer trust in leakage diagnostics. Full article

Background/Objectives: Publicly available open databases offer advantages in terms of accessibility and transparency. However, their application in intensive care research remains limited. Therefore, in this study, we examined whether simple nationwide comparisons of intensive care unit (ICU) practice patterns are feasible using an open database. Methods: A multicenter, cross-sectional study was conducted using data from the Bed Function Report. ICU wards reimbursed under ICU admission fee types were included and classified as high-acuity or standard ICUs. The ward-level procedure frequencies of procedures, including mechanical ventilation, were calculated. Comparisons were performed according to ICU admission fee type and geographic region. Quasi-Poisson regression models with offsets for annual ICU admissions were applied, accounting for overdispersion. Results: A total of 602 ICUs were included in the study. Non-metropolitan ICUs demonstrated higher procedural rates for mechanical ventilation compared with metropolitan ICUs (rate ratio [RR], 1.11; 95% confidence interval [CI], 1.02–1.21). Standard ICUs consistently had lower procedural rates for mechanical ventilation than high-acuity ICUs (RR, 0.74; 95% CI, 0.68–0.81). Group analyses indicated that regional differences in procedure frequencies were evident in standard ICUs, but not in high-acuity ICUs. Conclusions: This study demonstrated the feasibility of comparing ICU practice patterns across different regions and facility types in Japan using a nationwide open public database. This approach may serve as an initial step in a stepwise research framework that links open-database profiling to patient-level analysis using more detailed data sources. Full article

Integrating third-party Machine Learning (ML) models into industrial Operational Technology (OT) creates a procurement deadlock: operators cannot verify vendor performance claims without sharing representative evaluation data with vendors, while vendors refuse to reveal proprietary model weights before purchase, rendering traditional safeguards such as Non-Disclosure Agreements technically unenforceable. This paper introduces a framework combining Zero-Knowledge Proofs (ZKPs) with smart contracts to enable trust-minimized, cryptographically verifiable competitive model procurement in Industrial Cyber-Physical Systems (ICPS). Vendors cryptographically prove that their model outperforms a legacy baseline without disclosing proprietary weights, a process we term cryptographic performance attestation, while the on-chain workflow automates escrow, proof verification, and best-vendor selection with arbiter-based dispute resolution. ZKP privacy is scoped to vendor model weights; operator-side evaluation-data confidentiality is managed separately via synthetic, de-identified, or public benchmark data. We analyze three ZKP workflow variations and evaluate them on consumer-grade hardware, achieving proving times of approximately three seconds and sub-dollar on-chain verification costs under Layer-2 fee assumptions for the recommended single-proof variation, while identifying computational trade-offs of recursive proof aggregation. The entire verification phase operates offline with no impact on real-time OT control paths, bridging the IT/OT pre-transaction trust gap while deferring artifact deployment to existing OT tooling. Full article

This study examines the impact of corporate governance on sustainability-related risk in Indian banks across crisis and post-crisis periods. Using data from 37 public and private banks between 2006 and 2018, it analyzes how board characteristics influence liquidity and solvency risk. Panel regressions and a decision tree-based machine learning approach reveal consistent results: director busyness is associated with higher liquidity risk, while higher director and auditor fees are linked to improved liquidity management. Smaller, more independent boards and higher director fees are associated with lower solvency risk. The findings contribute emerging-market evidence on the governance–risk nexus and offer policy implications for bank governance and financial stability. Full article

The rising usage of IoT devices in everyday life has formed smart cities that require the adoption of decentralized systems for a secure and transparent mechanism to manage asset exchange across automotive supply chains. Several existing Blockchain-based models built on public chains focus on traceability while overlooking scalability limits, transaction fees, conditional payment trust, or real-time delivery validation. We introduce DeFiTrustChain, a DeFi-enabled framework that combines free NFTs, escrow-based automation, and IoT verification within a Hyperledger Fabric network. It represents each vehicle using a unique NFT to capture the details of manufacturing and ownership, along with immutable asset verification. The payment release between stakeholders is governed by a dedicated escrow contract responsible for IoT-based delivery confirmation. The proposed framework ensures authenticated access and prevents identity misuse through integration of the Fabric Certificate Authority. The experimental results demonstrate the coherent and dependable execution of NFT creation, escrow enforcement, and IoT-triggered validation, with low local transaction processing time and consistent behavior across peers. Full article

Urban stormwater management presents significant challenges for municipalities seeking to balance environmental resilience with financial considerations and social equity. This study investigates the factors shaping residents’ willingness to pay (WTP) for a proposed stormwater management fee in Norwalk, Connecticut, within the context of local sustainability plans. A survey of 457 residents assessed demographics, personal beliefs, perceptions of benefits, risks, and WTP. Since participation was voluntary and open, an exact response rate could not be calculated, and the resulting respondent profile differed from city benchmarks. The results were analyzed using descriptive and inferential statistics alongside a Random Forest machine learning model assessing two payment scenarios, achieving classification accuracies above the majority-class baseline (approximately 60–68%). Across both scenarios, expectations of tangible and locally visible outcomes, including infrastructure upgrades and climate resilience improvements, were the strongest determinants of WTP. When respondents evaluated a specific fee amount rather than a general modest fee, concerns about affordability and program effectiveness became more influential and revealed the conditional nature of financial support. The findings illustrate the value of machine learning for analyzing public attitudes toward environmental finance and highlight how policy framing, transparency, and communication shape acceptance of sustainability measures. These insights provide a data-driven foundation for future research on public engagement and equity in local environmental policy and stormwater plan development. Full article

This paper tested the use of the surplus efficient frontier (a minimum tracking error portfolio selection method) to select the optimal hedging portfolio that replicates the best Mexican #4 lemon price in a t + 1 and t + 4 week hedging scenario. Using data on the nine most traded agricultural futures in the US from January 2000 to February 2025, we tested hedging effectiveness across 502 futures portfolios in a weekly backtest. The results suggest that a corn and wheat portfolio increases the hedging effectiveness of the lemon price by 0.7033 or 70.33%. A result that, including the impact of trading fees and taxes, leads to a reduction in income risk to a lemon seller in a t + 1 week hedging horizon. The results suggest that a public or private financial institution could take a short position in such a portfolio to provide a hedge at a price that finances the spot/future price difference at minimum cost to Mexican taxpayers. Full article

Our research aims to provide a comparative analysis of water governance components by presenting the complexity of water-covered land use by the extractive industry in terms of legal, economic, and environmental protection aspects in Poland and Malaysia, along with the corresponding regulations and their implications. This paper discusses the legal forms of land ownership and use, as well as the currently applied principles for calculating fees for using state-owned water covered land that contains mineral deposits. We also present a comparison of selected technologies for the extraction of sand and gravel aggregates under water with their environmental impact. This research highlights the need for specialized valuation frameworks tailored to the geological and regulatory landscape of Poland and Malaysia. We suggest that the market value of land located above a mineral deposit, calculated individually for each deposit-property, should serve as the basis for calculating the lease fee. This discussion should encompass not only the principles and methodology involved in estimating the magnitudes of lease rents on mining industry and its profitability, but also the identification and criteria for assessing the risks associated with ongoing or planned mining ventures and concerns about the protection of river ecosystems. Our research contributes in providing data to stakeholders on extractive industry that operates within flowing and standing inland waters. The key finding of our research is that, in our opinion, the water governance frameworks in Poland and Malaysia are inadequate for protecting public finances and for internalizing the environmental externalities inherent in the economics of mining. Full article

Because auditors have access to corporate information, a firm’s decision to protect material trade secrets should, in principle, not influence audit effort. We analyze the effects of trade secrecy protection on the audit fees, documenting that firms with redacted information pay significantly higher fees than those that do not redact information. In cross-sectional tests, we further document that the relationship between redaction and audit fees is significantly influenced by both auditor and client characteristics. Consistent with the premise that redaction increases the complexity of the audit—particularly if auditors view redacted disclosures as indicators of potential business or litigation risk—the regression results indicate that the main effect is moderated by auditor factors such as specialization, tenure, and quality, as well as client factors like existing relationships, bargaining power, and reporting quality. These insights contribute to ongoing discussions in audit policy by illustrating how confidential disclosure practices affect audit effort and costs. Overall, our results inform policymakers seeking to reconcile firms’ proprietary information protection with public interest in transparent and credible financial reporting. Full article

In-Lieu Fee programs are an important mechanism for compensatory mitigation in the United States and received wide-spread standardization after the regulatory mitigation rule change of 2008. On public lands, they are especially important for pooling funds from numerous small-scale impacts that might otherwise go unmitigated. This study examines the use cases of fee program credits on public lands since 2008. Using data from the Regulatory In-Lieu Fee and Bank Information Tracking System, I analyzed eleven active In-Lieu Fee programs approved post-2008 across 78 service areas, encompassing 1043 credit transactions. Transactions were categorized by credit amount, proportion, target ecosystems, and impact designations. The analysis highlights the influence of residential and commercial development, alongside resource extraction, as major contributors to fee program transactions, underscoring the program’s role in mitigating various development pressures. Residential, commercial, and government projects frequently co-occur within service areas, which can support policy planning to anticipate potential cumulative impacts and expected future impacts and credit demands. Furthermore, my analysis shows that impacts from resource extraction require proportionally larger offsets than those from residential or recreational activities. The findings suggest that programs on public lands can fill a niche distinct from mitigation banks, as they address a multitude of impacts while further allowing for the pooling of resources and funds from small-scale impacts, while the use of advance credits remains contentious for achieving no net loss. Full article