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"Driving factors"
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Spatiotemporal variability of drought in representative dry-hot valley regions of Southwest China
【Objective】Extreme weather events like drought are expected to become more frequent in the context of climate change. Studying and understanding its spatiotemporal variation and the underlying drivers is critical to mitigating its detrimental impact. This paper analyzes drought trends and their influencing factors in a representative region in Southwest China.【Method】The study region is the Jinsha-Yuan River dry-hot valley in Yunnan Province. Using land surface temperature and the normalized difference vegetation index, we constructed a temperature vegetation dryness Index (TVDI). We then integrated natural and anthropogenic factors, including annual mean temperature, precipitation, evapotranspiration, and nighttime light intensity, and applied piecewise linear regression, the Mann-Kendall test, Theil-Sen slope estimation, GeoDetector, and correlation analysis to investigate the spatiotemporal changes in TVDI from 2001 to 2020 in the region.【Result】Temporally, TVDI exhibited a generally decreasing trend though with fluctuations. However, a significant shift occurred in 2018, after which drought became worse. Spatially, 48.1% of the area experienced moderate to severe drought, with Yuanmou, Huaping, Binchuan, and Yongren counties in the Jinsha River Basin and areas along the Yuan River identified as drought hotspots, which require priority monitoring and control. In contrast, there was less drought in Northeast and Northwest of the region. Temperature, altitude and evapotranspiration were the natural factors that affected drought variation the most, while human influence was relatively minor compared to natural factors. 【Conclusion】From 2001 to 2020, drought conditions across the study area generally improved, though with notable spatial heterogeneity. Natural environmental variables played a key role in shaping drought dynamics, underscoring their importance in regional drought risk assessment and management.
Journal Article
Automotive ergonomics : driver-vehicle interaction
\"The name of Karl Benz, one of the father figures in the automotive industry, is quoted more than once in this book. This is not only because of his undoubted contribution during the initial phase of developments, but also because of the contrast of expectations by key figures such as himself against the established beliefs and practices of today. Common perception of what the automobile is and whom it is addressed to was significantly different back then. From the very few who could afford and the handful of those skilled enough to control such machines, within a few decades we were led to the generalization of the automobile, first in the US, then in Europe and post-WWII Japan\"-- Provided by publisher.
Book
Modeling the relationships among the driving factors of food waste in Indonesian city
Purpose: Food waste in cities has become a pressing issue not only in developed countries but also in developing countries like Indonesia. The main objectives of this study are to (1) identify the relationships among the driving factors of food waste in Indonesian cities by considering the perspective of food industry practitioners and academics and (2) model the relationships among the driving factors of food waste to reduce food waste in Indonesian cities.Design/methodology/approach: The driving factors of food waste were selected from literature reviews and corroborated using in-depth interviews with practitioners and academics. A combination of three methods comprising (1) Decision-Making Trial and Evaluation Laboratory (DEMATEL), (2) Interpretive Structural Modelling (ISM), and Matrice d'Impacts Croisés Multiplication Appliqué un Classement (MICMAC) were used to construct the hierarchical model the relationship among the driving factors of food waste based on their driving power and dependence power.Findings: There are three key players in the food waste chain in urban areas in Indonesia: households, restaurants, supermarkets/markets. Fifteen driver factors on food waste based on the supplier-input-process-output-customer (SIPOC) framework were identified. The relationship among the driving factors of food waste was constructed in a hierarchical structure of food waste in Indonesian cities. With this, strategic action is formulated to reduce food waste.Practical implications: The proposed model of the driving factors of food waste can inform the city government on how to manage food waste. Likewise, the findings can assist Indonesian households, restaurants, and markets/supermarkets in minimizing their food waste. The ISM approach's hierarchical structure allows practitioners to better identify the objectives for reducing food waste in Indonesian cities.Originality/value: Previous studies have not examined and specifically modeled the relationships among the driving factors of food waste in Indonesian cities with three main players, i.e., households, restaurants, and supermarkets or traditional markets.
Journal Article
Spatial and Temporal Changes and Driving Factors of Desertification Around Qinghai Lake, China
The area around Qinghai Lake is one of the most serious desertification areas on the Qinghai-Tibet Plateau. In this paper, combined with field investigation and indoor analysis, the classification and grading system of desertification around Qinghai Lake was established. On this basis, through remote sensing data processing and parameter inversion, the desertification monitoring index model was established. Based on the analysis of Landsat-5/TM remote sensing data from 1990 to 2020, the dynamic change characteristics of desertification land around Qinghai Lake in recent 30 years were obtained. The results show that the desertification area around Qinghai Lake was 1,359.62 km2, of which the light desertification land was the main one. The desertification spread in a belt around Qinghai Lake, concentrated in Ketu sandy area in the east, Ganzi River sandy area in the northeast, Bird Island sandy area in the northwest, and Langmashe sandy area in the southeast. From 1990 to 2000, the annual expansion rate of desertification around Qinghai Lake was 2.68%, the desertification spread rapidly, and light desertification land was the main part of desertification expansion. From 2000 to 2010, the annual expansion rate of desertification was only 0.83%, but severe desertification land and moderate desertification land developed more rapidly than in the previous period. From 2010 to 2020, the annual expansion rate of desertification was 2.66%, and the desertification was spreading rapidly, mainly with moderate desertification land and light desertification land. In the process of desertification land transfer around Qinghai Lake, the transfer of desertification land and non-desertification land was the main, accompanied by the mutual transformation of different levels of desertification land. The process of desertification around Qinghai Lake was essentially the result of natural and human factors. The special geographical location, climate changes, rodent damage, and human factors around Qinghai Lake were the main causes of desertification.
Journal Article
Handbook of Human Factors for Automated, Connected, and Intelligent Vehicles
Automobile crashes are the seventh leading cause of death worldwide, resulting in over 1.25 million deaths yearly. Automated, connected, and intelligent vehicles have the potential to reduce crashes significantly, while also reducing congestion, carbon emissions, and increasing accessibility. However, the transition could take decades. This new handbook serves a diverse community of stakeholders, including human factors researchers, transportation engineers, regulatory agencies, automobile manufacturers, fleet operators, driving instructors, vulnerable road users, and special populations. The handbook provides information about the human driver, other road users, and human–automation interaction in a single, integrated compendium in order to ensure that automated, connected, and intelligent vehicles reach their full potential.
Features
Addresses four major transportation challenges—crashes, congestion, carbon emissions, and accessibility—from a human factors perspective
Discusses the role of the human operator relevant to the design, regulation, and evaluation of automated, connected, and intelligent vehicles
Offers a broad treatment of the critical issues and technological advances for the designing of transportation systems with the driver in mind
Presents an understanding of the human factors issues that are central to the public acceptance of these automated, connected, and intelligent vehicles
Leverages lessons from other domains in understanding human interactions with automation
Sets the stage for future research by defining the space of unexplored questions
eBook
Nonuniform variations of drought driven by spatially heterogeneous climate changes
Under global warming, the understanding of regional drought heterogeneity, drivers, and future persistence remains limited. Utilizing the Temperature Vegetation Dryness Index (TVDI, 2001–2020) and combining Theil–Sen trend analysis, Mann–Kendall test, partial correlation analysis, and Hurst exponent, this study analyzes global drought patterns, drivers, and persistence. Global drought changes exhibit significant spatial heterogeneity, exemplified by persistent intensification in Europe and an initial increase followed by subsequent mitigation in the Amazon. In terms of driving factors, drought is positively correlated with temperature in high-latitude regions (e.g. North America and Siberia), while it is primarily controlled by precipitation in arid regions. Compared to the period of 2001–2010, drought intensification became more widespread during 2011–2020, and its persistent nature suggests that most affected areas will continue to face sustained drought risks in the future. These findings underscore the necessity for region-specific adaptation strategies and provide valuable insights for drought risk assessment.
Journal Article
Industry 4.0: driving factors and impacts on firm’s performance: an empirical study on China’s manufacturing industry
The Industry 4.0 is important for China to achieve industrial upgrading and promoting the quality of manufacturing development. This paper investigates the driving force of the Industry 4.0 in China’s manufacture industry, and evaluates the impact of Industry 4.0 on firm’s performance. First, a textual mining is conducted to identify 460 companies that are implementing Industry 4.0 strategy, and then a Probit model is adopted to examine the driving forces of Industry 4.0. Through the propensity scores matching difference-in-difference method, the impacts of Industry 4.0 on firm’s performance are evaluated. The results reveal that private and large companies show a higher motivation to promote the Industry 4.0 strategy, and government subsidies have no significant impact on firm’s Industry 4.0 decision. The implementation of Industry 4.0 can significant improves firm’s financial performance, innovation activities and stock returns, but has no significant impact on supply chain efficiency. In addition, the adoption of Industry 4.0 has positive impact on firm’s information transparency grade.
Journal Article
Factors influencing Industry 4.0 adoption
PurposeThe digital transformation towards Industry 4.0 (I4.0) has become imperative for manufacturers, as it makes them more flexible, agile and responsive to customers. This study aims to identify the factors influencing the manufacturing firms’ decision to adopt I4.0 and develop a triadic conceptual model that explains this phenomenon.Design/methodology/approachThis study used a qualitative exploratory study design based on multiple case studies (n = 15) from the manufacturing industry in Malaysia by conducting face-to-face interviews. The data were analysed using NVivo. The conceptual model was developed based on grounded theory and deductive thematic analysis.FindingsResults demonstrate that driving, facilitating and impeding factors play influential roles in a firms’ decision-making to adopt I4.0. The major driving factors identified are expected benefits, market opportunities, labour problem, customer requirements, competition and quality image. Furthermore, resources, skills and support are identified as facilitating factors and getting the right people, lack of funding, lack of knowledge, technical challenges, training the operators and changing the mindset of operators to accept new digital technologies are identified as impeding factors.Research limitations/implicationsDue to its qualitative design and limited sample size, the findings of this study need to be supplemented by quantitative studies for enhanced generalizability of the proposed model.Practical implicationsKnowledge of the I4.0 decision factors identified would help manufacturers in their decision to invest in I4.0, as they can be applied to balancing advantages and disadvantages, understanding benefits, identifying required skills and support and which challenges to expect. For policymakers, our findings identify important aspects of the ecosystem in need of improvement and how manufacturers can be motivated to adopt I4.0.Originality/valueThis study lays the theoretical groundwork for an alternative approach for conceptualizing I4.0 adoption beyond UTAUT (Unified Theory of Acceptance and Use of Technology). Integrating positive and negative factors enriches the understanding of decision-making factors for I4.0 adoption.
Journal Article
Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm
Reasonable forest fire management measures can effectively reduce the losses caused by forest fires and forest fire driving factors and their impacts are important aspects that should be considered in forest fire management. We used the random forest model and MODIS Global Fire Atlas dataset (2010~2016) to analyse the impacts of climate, topographic, vegetation and socioeconomic variables on forest fire occurrence in six geographical regions in China. The results show clear regional differences in the forest fire driving factors and their impacts in China. Climate variables are the forest fire driving factors in all regions of China, vegetation variable is the forest fire driving factor in all other regions except the Northwest region and topographic variables and socioeconomic variables are only the driving factors of forest fires in a few regions (Northwest and Southwest regions). The model predictive capability is good: the AUC values are between 0.830 and 0.975, and the prediction accuracy is between 70.0% and 91.4%. High fire hazard areas are concentrated in the Northeast region, Southwest region and East China region. This research will aid in providing a national-scale understanding of forest fire driving factors and fire hazard distribution in China and help policymakers to design fire management strategies to reduce potential fire hazards.
Journal Article