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Green Public Procurement (GPP) is a crucial way to promote producing green products, but its relationship with corporate pollution emissions needs to be verified. This study aims to evaluate the environmental effects of the policy by analyzing how GPP influences corporate environmental pollution.

This study is based on extensive sample data of Chinese industrial enterprises from 2001 to 2010, using China’s first GPP list as an exogenous policy. The authors have established a differential model to explore the impact of GPP on corporate environmental pollution and its underlying mechanisms.

GPP significantly reduces the sulfur dioxide (SO2) emissions of enterprises. Verify the robustness of this conclusion by replacing variables, excluding other policy interventions that reduce selfselection bias, and conducting placebo testing. GPP encourages regulated enterprises to improve their production processes, drive clean production with green technology innovation, optimize energy structure, improve energy efficiency and reduce their emissions. The environmental cleaning effect of GPP is more significant in eastern and central China large and medium-sized urban areas. GPP has more effectively reduced SO2 emissions from private capital-intensive and heavily polluting enterprises.

This paper constructs a difference-in-differences model to study China’s first GPP list in 2006. It explores how GPP policies affect corporate pollution reduction. The findings enrich GPP research in China and emerging economies. Moreover, unlike existing studies on corporate pollution subject to environmental regulation, this paper focuses on how corporate pollution reduction is affected by demand-driven GPP policies, expanding the theoretical research.

To supply temporary pressure testing devices with favorable performance for emergency environments, this paper aims to present a pressure sensor with a central boss and straight-annular grooves. The structural feature is modeled and optimized by neural network-based method, and the device prototype is fabricated by 3D printing techniques.

The study initially compares mechanical properties of the proposed structure with two conventional designs using finite element analysis. The impacts from structural dimensions on sensor performance are modeled using a Backpropagation neural network and optimized through genetic algorithms. The sensing diaphragm is fabricated using stereolithography (SLA) 3D printing, while the piezoresistors and necessary interconnects are realized with screen printing techniques.

The experimental results demonstrate that the fabricated sensor exhibits a sensitivity of 2.8866 mV/kPa and a nonlinearity of 6.81% within the pressure range of 0–100 kPa. This performance is an improvement of 118% in sensitivity and a decrease of 54% in nonlinearity compared to flat diaphragm structure, highlighting the effectiveness of proposed diaphragm configuration.

This research offers a holistic methodology that encompasses the structural design, optimization and fabrication of pressure sensors. The proposed diaphragm and corresponding modelling method can provide a practical approach to enhance the measurement capabilities of pressure sensors. By leveraging SLA printing for diaphragm and screen printing for circuit, the prototype can be produced in a timely manner.

This study aims to examine the impact of climate change on the total factor productivity (TFP) of tourism in Chinese cities. Using temperature and precipitation as proxies for climate change, the research analyzes both the direct negative effects of climate change on tourism productivity and the positive spillover effects on neighboring cities. In addition, it investigates how geographic location and economic development contribute to the variation in these effects. The study also explores the mechanisms through which government intervention and industrial structure upgrading influence these impacts.

This study uses a spatial Durbin model to analyze the relationship between climate change and tourism TFP in 287 Chinese cities from 2000 to 2020. Panel data is used, with temperature and precipitation serving as proxies for climate change. The model evaluates both the direct and spillover effects of climate change on tourism productivity, while also analyzing the mechanisms through which government intervention and industrial upgrading affect these relationships. The study further considers how geographic location and economic development impact the results.

This study finds that climate change directly reduces tourism TFP, while generating positive spillover effects for neighboring cities. Cities in the eastern and more economically developed regions are more sensitive to climate change, experiencing stronger impacts compared to cities in central and western regions. The findings suggest that government intervention and industrial structure upgrading are important mechanisms through which climate change affects tourism productivity in Chinese cities.

This research fills a gap in the literature regarding how climate change affects tourism productivity in developing countries, particularly in China. By applying a spatial Durbin model and panel data analysis, the study provides empirical evidence on both the direct and spillover effects of climate change on tourism productivity. It highlights the critical role of government intervention and industrial upgrading as mechanisms shaping the impact of climate change, offering new insights for policymakers and tourism businesses to address the challenges posed by climate change and enhance productivity and competitiveness.

本研究旨在探讨气候变化对中国城市旅游全要素生产率的影响。通过温度和降水量作为气候变化的代理变量, 研究分析了气候变化对旅游全要素生产率的直接抑制效应及其对邻近城市的积极溢出效应。此外, 研究考察了地理位置与经济发展水平如何导致这些效应的异质性。通过分析政府干预和产业结构升级的机制, 本研究为气候变化影响旅游全要素生产率的机制分析提供了理论支持, 为提升发展中国家旅游竞争力提供了指导。

本研究采用空间杜宾模型分析2000年至2020年期间, 中国287个城市的气候变化与旅游全要素生产率之间的关系。研究使用面板数据, 温度和降水量作为气候变化的代理变量。模型分析了气候变化对旅游全要素生产率的直接效应与溢出效应, 并研究了政府干预与产业结构升级的机制效应。研究还考察了基于地理位置与经济发展水平的异质性影响, 提供了气候变化对城市旅游全要素生产率影响的综合分析。

气候变化直接抑制旅游全要素生产率, 同时对邻近城市产生积极的溢出效应。东部城市及高经济水平地区对气候变化更为敏感, 影响强于中西部地区。研究发现, 政府干预与产业结构升级是气候变化影响中国城市旅游全要素生产率的关键机制。

本研究填补了气候变化如何影响发展中国家, 尤其是中国, 旅游全要素生产率领域的研究空白。通过运用空间杜宾模型和面板数据分析, 提供了气候变化对旅游全要素生产率的直接效应与溢出效应的实证证据。研究强调了政府干预和产业结构升级作为气候变化影响旅游全要素生产率的主要机制。通过关注区域异质性与经济发展水平, 本研究为旅游企业与政策制定者应对气候变化挑战,提升生产力和竞争力提供了新的思路。

Este estudio examina el impacto del cambio climático en la productividad total de los factores (PTF) del turismo en las ciudades chinas. Utilizando la temperatura y las precipitaciones como indicadores del cambio climático, la investigación analiza tanto los efectos negativos directos del cambio climático sobre la productividad del turismo como los efectos indirectos positivos sobre las ciudades vecinas. Además, investiga cómo la ubicación geográfica y el desarrollo económico contribuyen a la variación de estos efectos. El estudio también explora los mecanismos a través de los cuales la intervención gubernamental y la mejora de la estructura industrial influyen en estos impactos.

Este estudio utiliza un modelo espacial de Durbin (SDM) para analizar la relación entre el cambio climático y la productividad total de los factores del turismo en 287 ciudades chinas entre 2000 y 2020. Se emplean datos de panel, en los que la temperatura y las precipitaciones sirven como variables sustitutivas del cambio climático. El modelo evalúa tanto los efectos directos como los indirectos del cambio climático sobre la productividad del turismo, al tiempo que analiza los mecanismos a través de los cuales la intervención gubernamental y la modernización industrial afectan a estas relaciones. El estudio examina además cómo influyen en los resultados la ubicación geográfica y el desarrollo económico.

El estudio concluye que el cambio climático reduce directamente la productividad total de los factores del turismo, al tiempo que genera efectos indirectos positivos para las ciudades vecinas. Las ciudades de las regiones orientales y económicamente más desarrolladas son más sensibles al cambio climático y experimentan impactos más fuertes que las ciudades de las regiones centrales y occidentales. Los resultados sugieren que la intervención gubernamental y la mejora de la estructura industrial son mecanismos importantes a través de los cuales el cambio climático afecta a la productividad del turismo en las ciudades chinas.

Esta investigación llena un vacío en la literatura sobre cómo el cambio climático afecta a la productividad del turismo en los países en desarrollo, especialmente en China. Aplicando un modelo espacial de Durbin y un análisis de datos de panel, el estudio aporta pruebas empíricas sobre los efectos directos y indirectos del cambio climático en la productividad del turismo. Destaca el papel decisivo de la intervención pública y la modernización industrial como mecanismos que determinan el impacto del cambio climático, ofreciendo nuevas perspectivas a los responsables políticos y las empresas turísticas para afrontar los retos que plantea el cambio climático y mejorar la productividad y la competitividad.

Unlike previous research that primarily utilized structural equation modelling (SEM) to evaluate safety hazards in subway projects, this research aims to utilize a hybrid approach to investigate and scrutinize the key indicators of safety hazards leading to accidents, thereby hindering the progress of subway projects in China, taking into cognizance the multiple stakeholder’s perspective.

By administering a survey questionnaire to 373 highly involved stakeholders in subway projects spanning Changsha, Beijing and Qingdao, China, our approach incorporated a four-staged composite amalgamation of exploratory factor analysis (EFA), confirmatory factor analysis (CFA), covariance-based structural equation modelling (CB-SEM) and artificial neural network (ANN) to develop an optimized model that determines the causal relationships and interactions among safety hazards in subway construction projects.

The optimized model delineated the influence of individual safety hazards on subway projects. The feasibility and applicability of the model developed was demonstrated on an actual subway project under construction in Changsha city. The outcomes revealed that the progress of subway projects is significantly influenced by risks associated with project management, environmental factors, subterranean conditions and technical hazards. In contrast, risks related to construction and human factors did not exhibit a significant impact on subway construction progress.

While our study provides valuable insights, it is important to acknowledge the limitation of relying on theoretical approaches without empirical validation from experiments or the field. In future research, we plan to address this limitation by assessing the SEM using empirical data. This will involve a comprehensive comparison of outcomes derived from CB-SEM with those obtained through SEM-ANN methods. Such an empirical validation process is crucial for enhancing the overall efficiency and robustness of the proposed methodologies.

The established hybrid model revealed complex non-linear connections among indicators in the intricate project, enabling the recognition of primary hazards and offering direction to improve management of safety in the construction of subways.

Addressing integrity, flexibility and interoperability challenges in enterprise information systems (EISs) is often hindered by the “three-fit” barrier, which encompasses vertical, horizontal and transversal fit problems. To overcome these obstacles, we propose solutions aimed at defining the business view of EIS. This study addresses these issues by proposing solutions tailored to the business view of EIS. Specifically, it introduces the core ontology of sensitive business processes (COSBP), a conceptual framework designed to formalize and define the multidimensional dimensions of sensitive business processes (SBPs). By providing a unified structure of central concepts and semantic relationships, COSBP enhances both knowledge management (KM) and business process management (BPM) in organizational contexts.

This paper adopts the design science research methodology covering the phases of a design-oriented research project that develops new artifacts, such as the COSBP ontology, based on SBP modeling requirements. Following a formal multi-level, multi-component approach, COSBP is structured into sub-ontologies across different abstraction levels. Built upon the Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) foundational ontology, COSBP integrates and extends core concepts from core domain ontologies in business processes. The framework specifies six key modeling dimensions of SBPs – functional, organizational, behavioral, informational, intentional and knowledge – each represented as a distinct class of ontological modules (OMs).

COSBP offers a semantically rich and precise framework for modeling SBPs, addressing complexity and ambiguity in conceptual modeling. It supports the creation of expressive and effective SBP models while enabling consensus-driven representation at a generic level. Additionally, COSBP serves as a foundation for extending modeling notations and developing tools that align with these notations. Its application in enterprise environments improves the integration, adaptability and interoperability of EISs, ultimately enhancing organizational processes and decision-making.

The development of the COSBP ontology holds considerable potential for application in various industries beyond its original focus on business process management and KM. The ontology’s capability to semantically model sensitive, knowledge-intensive and dynamic processes can be extended to other real-life scenarios in other complex domains and sectors – for example, finance and banking, government and public services, insurance, manufacturing and supply chain management, retail, E-commerce, logistics and transportation crisis management, government and public services, higher education and so on. By integrating artificial intelligence (AI) with the COSBP ontology, we aim to enable more intelligent decision-making, process monitoring and improved management of SBPs in knowledge-driven domains.