bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimation of the Mass Concentration of Volcanic Ash Using Ceilometers: Study of Fresh and Transported Plumes from La Palma Volcano
Authors: Bedoya-Velásquez Andres E. , Hoyos-Restrepo Manuela , Barreto Africa , García Rosa D. , Romero-Campos Pedro Miguel , García Omaira , Ramos Ramón , Roininen Reijo , Toledano Carlos , Sicard Michaël , Ceolato Romain
Published: November 2022

This study presents a synergistic approach to the study of the aerosol optical and microphysical properties measured in La Palma, Spain, during the 2021 eruption of the Cumbre Vieja volcano (from 19 September to 13 December 2021). This study aims to characterize the different phases of the volcanic eruption using the spatio-temporal evolution of the event together with the mass concentration quantification of four different atmospheric layers. The impact of the plume’s pathway that reached the South of France is analyzed. Here, passive and active remote sensors were used, namely CL51 and CL61 ceilometers and AERONET sunphotometers. The attenuated backscattering ranged from 0.8 to 9.1 × 10−6 (msr)−1 and the volume depolarization ratio measured nearby the volcano was up to 0.3. The ash plume remained within the first 4 km agl, with intense episodes that reached mean aerosol optical depth values of up to 0.4. Thirteen study cases were selected where coarse mode was dominant over fine mode. For the data selection, the fine and coarse lidar ratios found were 3.9 ± 0.8 and 21.0 ± 3.8 sr in the north and 6.9 ± 1.8 and 30.1 ± 10.3 sr in the south. The ash mass concentration reached moderate levels with maximum values of up to 313.7 μgm−3.
Downscaling of SMAP Soil Moisture Data by Using a Deep Belief Network
Authors: Cai Yulin , Fan Puran , Lang Sen , Li Mengyao , Muhammad Yasir , Liu Aixia
Published: November 2022

The spatial resolution of current soil moisture (SM) products is generally low, consequently limiting their applications. In this study, a deep belief network-based method (DBN) was used to downscale the Soil Moisture Active Passive (SMAP) L4 SM product. First, the factors affecting soil surface moisture were analyzed, and the significantly correlated ones were selected as predictors for the downscaling model. Second, a DBN model was trained and used to downscale the 9 km SMAP L4 SM to 1 km in the study area on 25 September 2019. Validation was performed using original SMAP L4 SM data and in situ measurements from SM and temperature wireless sensor network with 34 sites. Finally, the DBN method was compared with another commonly used machine learning model-random forest (RF). Results showed that (1) the downscaled 1 km SM data are in good agreement with the original SMAP L4 SM data and field measured data, and (2) DBN has a higher correlation coefficient and a lower root mean square error than those of RF. The coefficients of determination for fitting the two models with the measured data at the site were 0.5260 and 0.4816, with relative mean square errors of 0.0303 and 0.0342 m3/m3, respectively. The study also demonstrated the applicability of the DBN method to AMSR SM data downscaling besides SMAP. The proposed method can provide a framework to support future hydrological modeling, regional drought monitoring, and agricultural research.
Special Issue “Ground Penetrating Radar (GPR) Applications in Civil Infrastructure Systems”
Authors: Zayed Tarek , Dawood Thikra , Abouhamad Mona , Alsharqawi Mohammed
Published: November 2022

This Special Issue includes a collection of papers that address the practical applications of GPR to various civil infrastructure systems [...]
Inverted Algorithm of Groundwater Storage Anomalies by Combining the GNSS, GRACE/GRACE-FO, and GLDAS: A Case Study in the North China Plain
Authors: Shen Yifan , Zheng Wei , Zhu Huizhong , Yin Wenjie , Xu Aigong , Pan Fei , Wang Qiang , Zhao Yelong
Published: November 2022

As the largest groundwater drainage region in China, the per capita water resources in the North China Plain (NCP) account for only one-seventh of the country’s available water resources. Currently, the NCP is experiencing a serious water shortage due to the overexploitation of groundwater resources and a subsequent series of natural disasters. Thus, accurate regional assessments and effective water resource management policies are of critical importance. To accomplish this phenomenon, the daily terrestrial water storage anomaly (TWSA) over the NCP is calculated from the combination of the GNSS vertical deformation sequences (seasonal items) and GRACE (trend items). The groundwater storage anomaly (GWSA) in the NCP is obtained by subtracting the canopy water, soil water, and snow water equivalent components from the TWSA. The inversion results of this study are verified by comparisons with the Global Land Data Assimilation System (GLDAS) data products. The elevated annual amplitude areas are located in Beijing and Tianjin, and the Pearson correlation coefficient (PCC), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) between the two GWSA results are 0.67, 4.01 cm, and 0.61, respectively. This indicates that the methods proposed in this study are reliable. Finally, the groundwater drought index was calculated for the period from 2011 to 2021, and the results showed that 2019 was the driest year, with a drought severity index value of −0.12, indicative of slightly moderate drought conditions. By calculating and analyzing the annual GWSA, this work shows that the South–North Water Transfer Project does provide some regional drought mitigation.
Spatial Effects of Landscape Patterns of Urban Patches with Different Vegetation Fractions on Urban Thermal Environment
Authors: Zhang Yu , Wang Yuchen , Ding Nan
Published: November 2022

The landscape patterns of urban green spaces have been proven to be important factors that affect urban thermal environments. However, the spatial effect of the landscape patterns of urban patches with different vegetation densities on land surface temperature (LST) has not been investigated in detail. In this study, the built-up area of Xuzhou City was taken as the study region, and the four phases of Landsat 8 images and their corresponding ground observations from 2014 to 2020 were selected as the basic data. Normalized spectral mixture analysis and an improved mono-window algorithm were used to invert the vegetation component fraction (VF) and LST maps of the study area, respectively, and the surface patches were classified into five levels according to the VF values, from low to high. Four landscape-level and five class-level metrics were then selected to represent the landscape characteristics of each VF-level patch. The tested values of 60 and 780 m were regarded as the best grain size and spatial extent, respectively, in the calculation of all landscape metrics of ALL VF-level patches (VFLM) using the moving-window method. The results of bivariate Moran’s I for VFLM and LST showed the following: (1) for landscape-level metrics, only the Shannon diversity index and patch diversity have substantial negative spatial correlations with LST (with average |Moran’s I| < 0.2), indicating that the types of VF levels and the number of patches exert weak negative effects on the thermal environment for a certain area; (2) for class-level metrics such as percentage of landscape, patch cohesion index, largest patch index, landscape shape index, and aggregation index, only the class-level metrics of sub-high VF (LV4) and extreme-high (LV5) VF levels patches have significant negative spatial correlations with LST (with high Moran’s I value, and high–high and low–high distributions in local indications of spatial association cluster maps), indicating that only the patches of high VF levels can effectively alleviate LST and that patch proportion, natural connectivity degree, predominance degree, shape complexity, and aggregation degree are important landscape factors for regulating the thermal environment. Principal component analysis and multiple linear regression were applied to determine the impact weights of the class-level VFLMs of LV4 and LV5 patches on LST, which revealed the contributions of these landscape metrics to mitigating the urban heat island effect (UHI). These results signify the importance of and differences in the spatial patterns of various VF-level patches for UHI regulation; these patterns can provide new perspectives and references for urban green space planning and climate management.
Analyzing Spatial Variations of Cloud Attenuation by a Network of All-Sky Imagers
Authors: Blum Niklas Benedikt , Wilbert Stefan , Nouri Bijan , Stührenberg Jonas , Lezaca Galeano Jorge Enrique , Schmidt Thomas , Heinemann Detlev , Vogt Thomas , Kazantzidis Andreas , Pitz-Paal Robert
Published: November 2022

All-sky imagers (ASIs) can be used to model clouds and detect spatial variations of cloud attenuation. Such cloud modeling can support ASI-based nowcasting, upscaling of photovoltaic production and numeric weather predictions. A novel procedure is developed which uses a network of ASIs to model clouds and determine cloud attenuation more accurately over every location in the observed area, at a resolution of 50 m × 50 m. The approach combines images from neighboring ASIs which monitor the cloud scene from different perspectives. Areas covered by optically thick/intermediate/thin clouds are detected in the images of twelve ASIs and are transformed into maps of attenuation index. In areas monitored by multiple ASIs, an accuracy-weighted average combines the maps of attenuation index. An ASI observation’s local weight is calculated from its expected accuracy. Based on radiometer measurements, a probabilistic procedure derives a map of cloud attenuation from the combined map of attenuation index. Using two additional radiometers located 3.8 km west and south of the first radiometer, the ASI network’s estimations of direct normal (DNI) and global horizontal irradiance (GHI) are validated and benchmarked against estimations from an ASI pair and homogeneous persistence which uses a radiometer alone. The validation works without forecasted data, this way excluding sources of error which would be present in forecasting. The ASI network reduces errors notably (RMSD for DNI 136 W/m2, GHI 98 W/m2) compared to the ASI pair (RMSD for DNI 173 W/m2, GHI 119 W/m2 and radiometer alone (RMSD for DNI 213 W/m2), GHI 140 W/m2). A notable reduction is found in all studied conditions, classified by irradiance variability. Thus, the ASI network detects spatial variations of cloud attenuation considerably more accurately than the state-of-the-art approaches in all atmospheric conditions.
Integrating Gaussian Mixture Dual-Clustering and DBSCAN for Exploring Heterogeneous Characteristics of Urban Spatial Agglomeration Areas
Authors: Xiao Tong , Wan Yiliang , Jin Rui , Qin Jianxin , Wu Tao
Published: November 2022

Exploring the heterogeneous characteristics of the urban expansion process is essential for understanding the dynamics of the urban spatial structure. Many studies focused on depicting the spatio-temporal characteristics based on urban expansion patches. However, measuring heterogeneous characteristics of urban expansion from agglomeration areas comprising the expanded urban construction land patches have not been adequately explored. This study presents a novel approach and two improved indices for characterizing the heterogeneity of urban spatial agglomeration areas during urban expansion. Firstly, we proposed a Gaussian mixture model considering multiple constrains and density-based spatial clustering of applications with noise (DBSCAN) integration method to identify and extract the urban agglomeration areas automatically. Secondly, the gradient analysis and the compact index using the inverse “S” function are introduced to explore the spatio-temporal characteristics from a macrocosmic perspective. Finally, the compactness index (NCI) and normalized dispersion index (NDIS) are improved based on agglomeration area data. The microcosmic heterogeneous characteristics are measured by these two improved indices and the positional offset characteristics indices (POCIS). The method was implemented in the urban area of Changsha, Hunan Province, China in 2005, 2010, and 2015. The results show that (1) compared to that in the Changsha City Master Plan (2003–2020), the recognition rate was higher in the agglomeration areas than others. (2) The overall expansion trend in Changsha transitioned toward decentralization, making Changsha a polycentric city. (3) The agglomeration of urban expansion in the east-west direction became compact; that in the north-south direction became looser; most clusters expanded to the west and a new sub-center would appear. The proposed method can effectively characterize their heterogeneity, which can provide valuable references for urban planning and policymaking.
AICCA: AI-Driven Cloud Classification Atlas
Authors: Kurihana Takuya , Moyer Elisabeth J. , Foster Ian T.
Published: November 2022

Clouds play an important role in the Earth’s energy budget, and their behavior is one of the largest uncertainties in future climate projections. Satellite observations should help in understanding cloud responses, but decades and petabytes of multispectral cloud imagery have to date received only limited use. This study describes a new analysis approach that reduces the dimensionality of satellite cloud observations by grouping them via a novel automated, unsupervised cloud classification technique based on a convolutional autoencoder, an artificial intelligence (AI) method good at identifying patterns in spatial data. Our technique combines a rotation-invariant autoencoder and hierarchical agglomerative clustering to generate cloud clusters that capture meaningful distinctions among cloud textures, using only raw multispectral imagery as input. Cloud classes are therefore defined based on spectral properties and spatial textures without reliance on location, time/season, derived physical properties, or pre-designated class definitions. We use this approach to generate a unique new cloud dataset, the AI-driven cloud classification atlas (AICCA), which clusters 22 years of ocean images from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua and Terra instruments—198 million patches, each roughly 100 km × 100 km (128 × 128 pixels)—into 42 AI-generated cloud classes, a number determined via a newly-developed stability protocol that we use to maximize richness of information while ensuring stable groupings of patches. AICCA thereby translates 801 TB of satellite images into 54.2 GB of class labels and cloud top and optical properties, a reduction by a factor of 15,000. The 42 AICCA classes produce meaningful spatio-temporal and physical distinctions and capture a greater variety of cloud types than do the nine International Satellite Cloud Climatology Project (ISCCP) categories—for example, multiple textures in the stratocumulus decks along the West coasts of North and South America. We conclude that our methodology has explanatory power, capturing regionally unique cloud classes and providing rich but tractable information for global analysis. AICCA delivers the information from multi-spectral images in a compact form, enables data-driven diagnosis of patterns of cloud organization, provides insight into cloud evolution on timescales of hours to decades, and helps democratize climate research by facilitating access to core data.
Using Optical Flow Trajectories to Detect Whitecaps in Light-Polluted Videos
Authors: Hu Xinyao , Yu Qianxiang , Meng Ankang , He Chenglong , Chi Shukai , Li Ming
Published: November 2022

Whitecap formation is an important factor in the exchange of momentum, heat, and gas on the ocean surface. The long-term measurement of whitecaps is necessary to deepen our understanding of the mechanisms of ocean surface motion. However, traditional detection methods are highly sensitive to illumination. Under various illumination conditions, significant light pollution may be introduced into images. The poor performance caused by using images degraded with light pollution is not conducive to automated long-term whitecap measurement. In this study, we propose a two-step method for the detection of whitecaps under various illumination conditions. An abnormal detection method based on previous whitecap detection methods for the accurate detection of whitecaps in light-polluted areas is proposed as the first step. Using the detection results, we propose a post-processing method based on optical flow trajectories at two sampling rates to separate actual whitecap components in images containing false positives. Experiments show that the method proposed in this study can more accurately detect whitecaps in images with light pollution when compared to existing methods.
Glacier Changes in India’s Dhauliganga Catchment over the Past Two Decades
Authors: Ali Nauman , Ye Qinghua , Zhang Xueqin , Ji Xinhui , Hu Yafan , Zhu Liping , Ali Arslan
Published: November 2022

The rapid melting of glaciers has led to severe glacial-hydrological hazards in the Himalayas. An extreme example occurred on 7 February 2021, when a catastrophic mass flow descended from the Ronti glacier at Chamoli, Indian Himalaya, causing widespread devastation, with more than 200 people killed or missing, as well as severe damage to four hydropower projects. To disclose what happened to the Ronti glacier over the past several decades, here, we focused on glacier changes in the Dhauliganga catchment in Uttarakhand, India, over the past two decades. Another five glaciers in the catchment were also studied to map the regional detailed glacier changes. Our achievements are summarized as follows. (1) Based on Landsat images, we constructed two glacier inventories for the catchment in 2001 and 2020. We mapped nearly 413 debris-free glaciers in the catchment between 2001 and 2020 and analyzed the glacier area change at basin and altitude levels. (2) Debris-free glacier area decreased from 477.48 ± 35.23 km2 in 2001 to 418.52 ± 36.18 km2 in 2020, with a reduction of 58.95 km2 or 12.35% over the past two decades. (3) The geodetic mass balance was −0.27± 0.10 m w.e.a−1, with a glacier mass change of −0.12 Gt. a−1 from 2000 to 2013. Based on the surface elevation difference between the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) footprints (acquired from 2018 to 2021) and the National Aeronautics and Space Administration (NASA) DEM from 2000 to 2021, the average glacier geodetic mass balance was −0.22 ± 0.005 m w.e.a−1, and glacier mass change was −0.10 Gt a−1. (4) Our results were cross verified by available published elevation difference datasets covering multiple temporal periods, where mass balance was by −0.22 ± 0.002 m w.e.a−1 from 1975 to 2000 and −0.28 ± 0.0001 w.e.a−1 from 2000 to 2020. (5) Glacier 1 and Glacier 2, the two largest glaciers in the catchment, experienced a decreasing melt rate from 2000 to 2020, while Glacier 3, Glacier 4, and Glacier 5 demonstrated an increasing melt rate. However, Glacier 6, also known as the collapsed Ronti glacier, had a negative mass balance of −0.04 m w.e.a−1 from 2000 to 2005 and turned positive from 2005 onward with 0.06 m w.e.a−1 from 2005 to 2010, 0.19 m w.e.a−1 from 2010 to 2015, and 0.32 m w.e.a−1 from 2015 to 2020. We postulate that the Ronti glacier collapsed solely because of the significant mass accumulation observed between 3700 to 5500 m a.s.l. Our study helps to understand the collapsed glacier’s mass changes over the past two decades and highlights the necessity to monitor mass-gaining glaciers from space to forecast the risks of disasters.
Advanced Control Systems in Industry 5.0 Enabling Process Mining
Authors: Massaro Alessandro
Published: November 2022

This paper merges new research topics in Industry 5.0 using the Business Process Modeling and Notation (BPMN) approach able to integrate Artificial Intelligence (AI) in production processes. The goal is to provide an innovative approach to model production management in industry, adopting a new “proof of concept” of advanced Process Mining (PM) automatizing decisions and optimizing machine setting and maintenance interventions. Advanced electronic sensing and actuation systems, integrating supervised and unsupervised AI algorithms, are embedded in the PM model as theoretical process workflows suggested by a Decision Support System (DSS) engine enabling an intelligent decision-making procedure. The paper discusses, as examples, two theoretical models applied to specific industry sectors, such as food processing and energy production. The proposed work provides important elements of engineering management related to the digitalization of production process matching with automated control systems setting production parameters, thus enabling the self-adapting of product quality supervision and production efficiency in modern industrial systems.
A Blockchain-Based Product Traceability System with Off-Chain EPCIS and IoT Device Authentication
Authors: Li Lulu , Qu Huan , Wang Huaizhen , Wang Junyu , Wang Bozhi , Wang Wei , Xu Jinfei , Wang Zhihui
Published: November 2022

Blockchain-based traceability systems are a promising approach because they are decentralized, transparent, and tamper proof; however, if all traceability data are uploaded to a blockchain platform, it may affect the efficiency or even lead to data explosion. Additionally, it is difficult to guarantee the reliability of the original data source of massive Internet of Things (IoT) devices. Furthermore, when different enterprise nodes adopt different data storage structures, the costs that are associated with data sharing will increase. In this paper, we have proposed a trustworthy product traceability system that is based on hyperledger fabric and Electronic Product Code Information Service (EPCIS), which is not only capable of making products traceable, but it can also authenticate and authorize the IoT devices that are used for data collection. First, we adopted the on-chain and off-chain collaborative management mechanism in order to alleviate data explosion on the chain. Second, we proposed a scheme to authenticate and authorize devices based on blockchain. Third, we complied with EPCIS and Core Business Vocabulary (CBV) standards and provided the EPCIS location discovery service in order to improve the interactivity. Finally, we implemented and tested the proposed traceability system and compared it with the existing research. The proposed solution provides product information traceability, data tamper proofing, data confidentiality, and data source reliability.
TSD-Truncated Structurally Aware Distance for Small Pest Object Detection
Authors: Huang Xiaowen , Dong Jun , Zhu Zhijia , Ma Dong , Ma Fan , Lang Luhong
Published: November 2022

As deep learning has been successfully applied in various domains, it has recently received considerable research attention for decades, making it possible to efficiently and intelligently detect crop pests. Nevertheless, the detection of pest objects is still challenging due to the lack of discriminative features and pests’ aggregation behavior. Recently, intersection over union (IoU)-based object detection has attracted much attention and become the most widely used metric. However, it is sensitive to small-object localization bias; furthermore, IoU-based loss only works when ground truths and predicted bounding boxes are intersected, and it lacks an awareness of different geometrical structures. Therefore, we propose a simple and effective metric and a loss function based on this new metric, truncated structurally aware distance (TSD). Firstly, the distance between two bounding boxes is defined as the standardized Chebyshev distance. We also propose a new regression loss function, truncated structurally aware distance loss, which consider the different geometrical structure relationships between two bounding boxes and whose truncated function is designed to impose different penalties. To further test the effectiveness of our method, we apply it on the Pest24 small-object pest dataset, and the results show that the mAP is 5.0% higher than other detection methods.
Critical Vibration and Control of the Maglev High-Speed Motor Based on μ–Synthesis Control
Authors: Hu Yefa , Yang Kezhen , Wu Huachun , Guo Xinhua , Wang Nianxian
Published: November 2022

The Maglev motor has the characteristics of high-speed and high-power density, and is widely used in compressors, molecular pumps and other high-speed rotating machinery. With the requirements of miniaturization and high speed of rotating machinery, the rotor of the maglev motor will operate above the bending critical speed, and the critical vibration control of the flexible rotor is facing challenges. In order to solve the problem of the critical vibration suppression of the maglev high-speed motor, the system model of the maglev motor is established, the rotordynamics of the flexible rotor are analyzed and the rotor model is modal truncated to reduce the order. Then, the μ–controller is designed, and the weighting functions are designed to deal with the modal uncertainty. Finally, an experimental platform of the maglev motor with the flexible rotor is built to verify the effect of the μ–control on the suppression of the critical vibration of the maglev rotor.
Eddy Current Measurement for Planar Structures
Authors: Xia Zihan , Huang Ruochen , Chen Ziqi , Yu Kuohai , Zhang Zhijie , Salas-Avila Jorge Ricardo , Yin Wuliang
Published: November 2022

Eddy current (EC) testing has become one of the most common techniques for measuring metallic planar structures in various industrial scenarios such as infrastructures, automotive, manufacturing, and chemical engineering. There has been significant progress in measuring the geometry, electromagnetic properties, and defects of metallic planar structures based on electromagnetic principles. In this review, we summarize recent developments in EC computational models, systems, algorithms, and measurement approaches for planar structures. First, the computational models including analytical models, numerical methods, and plate property estimation algorithms are introduced. Subsequently, the impedance measurement system and probes are presented. In plate measurements, sensor signals are sensitive to probe lift-off, and various algorithms for reducing the lift-off effect are reviewed. These approaches can be used for measureing thickness and electromagnetic properties. Furthermore, defect detection for metallic plates is also discussed.
IBD: An Interpretable Backdoor-Detection Method via Multivariate Interactions
Authors: Xu Yixiao , Liu Xiaolei , Ding Kangyi , Xin Bangzhou
Published: November 2022

Recent work has shown that deep neural networks are vulnerable to backdoor attacks. In comparison with the success of backdoor-attack methods, existing backdoor-defense methods face a lack of theoretical foundations and interpretable solutions. Most defense methods are based on experience with the characteristics of previous attacks, but fail to defend against new attacks. In this paper, we propose IBD, an interpretable backdoor-detection method via multivariate interactions. Using information theory techniques, IBD reveals how the backdoor works from the perspective of multivariate interactions of features. Based on the interpretable theorem, IBD enables defenders to detect backdoor models and poisoned examples without introducing additional information about the specific attack method. Experiments on widely used datasets and models show that IBD achieves a 78% increase in average in detection accuracy and an order-of-magnitude reduction in time cost compared with existing backdoor-detection methods.
Interaction of Manganese Ions with Scheelite Surfaces and Its Effect on Collector Adsorption and Flotation
Authors: Wang Xiao , Wen Shuming , Zuo Qi , Liao Runpeng , Meng Shengbing , Tao Yuanyuan , Shen Zhihao , Feng Qicheng
Published: November 2022

Tungsten is a commercially important metal element that usually coexists with a variety of non-ferrous metals, which makes its extraction difficult. Scheelite is a commonly occurring tungsten-containing ore with the formula CaWO4. Improving the surface properties of scheelite to increase its adsorption of the collector for flotation separation is the focus of our current research. In this paper, the effects of manganese ions on scheelite flotation in benzohydroxamic acid (BHA) system were studied by micro-flotation tests, adsorption tests, fourier transform infrared spectroscopy (FTIR), zeta potential, and X-ray photoelectron spectroscopy (XPS) analysis. The addition of Mn2+ was found to improve the recovery of scheelite. The addition of Mn2+ greatly improved the recovery of scheelite. Infrared spectroscopy, adsorption tests, zeta potential measurements and XPS analysis all confirmed that BHA had a higher adsorption capacity and a stronger bond to the surface of scheelite after the addition of manganese ions, increasing the floatability of scheelite particles. Therefore, Mn2+ shows great potential for the improvement of the flotation index of scheelite in a system with BHA.
Challenges and How to Overcome Them in the Formulation and Implementation Process of a Sustainability Balanced Scorecard (SBSC)
Authors: Eifert Anna , Julmi Christian
Published: November 2022

To cope with the increasing importance of sustainability, the Sustainability Balanced Scorecard (SBSC) has been developed to support companies integrating the economic, social and environmental dimensions of sustainability into their business. However, the formulation and implementation process of an SBSC is confronted with multiple challenges that have to be dealt with. This article associates the challenges with four steps of the process in particular: (1) the conceptual integration of the three dimensions into the strategy, (2) the selection of the architecture of the SBSC, (3) the formulation of sustainable key performance indicators and the development of cause-effect chains, and (4) the implementation of the SBSC. For these steps, the article summarizes and outlines the identified challenges, discusses techniques, criteria, guidelines and success factors to overcome them and derives research gaps that need to be addressed. Hence, the article speaks to both researchers and practitioners. For researchers, the article’s contribution is to synthesize the findings of the literature and to identify research gaps. For practitioners, the article’s contribution is to provide a systematic process for companies to improve their sustainability management and performance.
Commuting to University: Self-Reported and Device-Measured Physical Activity and Sedentary Behaviour
Authors: Palma-Leal Ximena , Chillón Palma , Segura-Jiménez Víctor , Pérez-Bey Alejandro , Sánchez-Delgado Alejandro , Camiletti-Moirón Daniel
Published: November 2022

Background: Active commuting could provide an opportunity to counteract unhealthy behaviours, such as insufficient levels of Physical Activity (PA) and sedentary behaviour, which are major health problems in the university population. The aims of this study were to describe and compare self-reported and device-measured in commuting behaviours, PA, and sedentary behaviour in both trips (to and from university) by mode of commuting per weekday, and to identify associations between self-reported and device-measured of commuting behaviours, PA, and sedentary behaviour. Methods: After inclusion criteria, a total of 63 students (65.1% women) from a public university in Cádiz, Spain, participated in this study. Self-reported and device-measured information was used. Results: Commuting time, distance, and speed were lower in active commuters than public and private commuters in both trips (to and from university) (all, p < 0.001). Commuting energy expenditure per min was higher in active commuters than public and private commuters (all, p < 0.001). Active commuters presented significant differences (p < 0.05) with public and private commuters in all PA levels and sedentary behaviour in both trips (to and from university). Conclusions: Active commuting involved the highest levels of energy expenditure per min and could contribute 44% of the weekly PA recommendation for health benefits in university students.
Tourists’ Perceived Restoration of Chinese Rural Cultural Memory Space
Authors: Chang Xueyu , Li Zhenting
Published: November 2022

In the face of the growing demand for stress reduction among urban residents, research on the restorative effect of cultural environments and cultural landscape is currently limited. This paper aims to explore the perceived restoration of rural cultural memory space in a Chinese cultural context and to investigate the role of situational involvement and place attachment in this respect. The results show that rural cultural memory space can directly produce restorative effects, but each perceptual dimension has internal variability. According to appraisal theory and self-regulation theory, revealing the complex pathways of tourists’ perceptions of rural cultural memory space can be generated through a process of situational involvement and placing attachment to produce tourists’ restorative perceptions. The research results highlight the predictors of restorative environment in the context of the Chinese vernacular culture and provide references for rural tourism landscape design.
The Potential of Carbon Emissions Reductions of Public Bikes
Authors: Lu Ting , Xu Yan , Chen Linfan , Lu Lili , Ren Rui
Published: November 2022

The reduction of carbon emissions has become a heated background topic in the context of climate change. This paper estimates the potential for carbon reduction from the use of public bikes, on the basis of a travel mode choice model and a carbon emission calculation model. A probability model for the travel mode choice is built to predict travel demands of different modes, and is based on the Logit-based stochastic user equilibrium model. According to this, the generalized travel cost of choosing to walk increases with distance, but the cost of choosing a taxi decreases with distance. When the trip distance is 1.4 km, the walk cost equals to that of the taxi, while if the trip distance is smaller than 1.4 km, the probability of the walk is larger than of a taxi, and vice versa. The case of Ningbo is analyzed. Based on the monthly travel data, the travel characteristics of the public bikes are first analyzed; these indicate that the medium travel distance is 1.44 km, and that the number of trips less than 1.6 km accounts for 70% of all trips. This reveals that the public bike trips are mainly short-distance and in workday rush hour. The related carbon emission reductions of Ningbo on average are 1.97 kg/person and 1.98 kg/km2, and the reductions are positively linearly related to the average hourly total turnover rate, which means the turnover rate is a great parameter to reflect the capability of carbon emission reductions.
Barriers to Adoption of Sustainable Procurement in the Nigerian Public Construction Sector
Authors: Oyewobi Luqman Oyekunle , Jimoh Richard Ajayi
Published: November 2022

Construction industry tenders are usually awarded to the lowest bidder, with emphasis being placed on the past, on price, quality, and timeliness, with little to no attention paid to the commitment of contractors to sustainability. It is not all construction firms and other stakeholders have an understanding of what sustainable procurement is about in the Nigerian construction industry, resulting in the limited adoption by those that understood it due to the level of risk they claimed it involved. This research examines the barriers to implementing sustainable procurement in the Nigerian construction industry by adopting a survey approach using self-administered questionnaires to obtain data from a purposively sampled group of 116 procurement practitioners in Abuja, Nigeria. The data were analyzed using descriptive statistics and exploratory factor analysis. Factor analysis was used in categorizing the barriers to sustainable procurement into four: attitude and poor fiscal incentive; financial restrictions; insufficient leadership and knowledge; and regulatory constraints. The result showed that there was no sustainable procurement regulatory framework in the country and that a lack of government commitment, poor economic conditions, and a lack of knowledge were the major barriers to the adoption of sustainable procurement in the Nigerian public construction sector. It is therefore suggested that to make the construction industry’s activities more sustainable, practitioners need to learn more about sustainability, and governments should make policies that encourage and support low tariffs and taxes on sustainable goods and provide government subsidies. The research adds to the ongoing discussion about sustainable procurement practices in developing economies. It does this by drawing on a variety of theoretical perspectives to give a deep understanding of the challenges of sustainability from the public sector’s point of view.
A Closer Look at Effective Intervention Methods to Reduce Household Solid Waste Generation in Japan
Authors: Saitoh Yoshinori , Tago Hiroshi , Kumagai Kimiyo , Iijima Akihiro
Published: November 2022

In many countries municipal solid waste (MSW) is expected to soon increase beyond the pace of population growth due to urbanization. To minimize its negative impact, MSW management needs to be advanced. We studied administrative awareness-raising projects aimed at reducing household solid waste (HSW), which accounts for a large portion of MSW. An online questionnaire survey was administered to local governments (LGs) in Japan to research the implementation status of the awareness-raising projects and estimate the waste reduction effect of intervention methods within those projects. Regarding social factors, multiple linear regression analysis showed significant negative relationships of HSW generation rate with the household population, total population, and waste charge system. Conversely, positive relationships were identified with age, the number of cars, income, and the frequency of collection. Intervention methods, such as briefing sessions, utilization of resident leaders, and mobile phone apps, were revealed to be effective; in contrast, information dissemination using the traditional intervention method was not. In particular, the utilization of resident leaders may be the most cost-effective, but some LGs seem to have abolished this form of intervention after its introduction 30 years ago due to lack of empirical evidence supporting its effectiveness.
Study on the Impact of Breakthrough and Incremental Innovation on Firm Capacity Utilization
Authors: Shen Yi , Yang Xiaoxin
Published: November 2022

Effectively resolving manufacturing overcapacity is an important way for China to stimulate economic vitality and achieve high-quality economic development. Although research on technological innovation to resolve overcapacity has attracted academic attention, less research has been conducted from the perspective of innovation intensity. This paper uses the panel data of 1447 A-share listed companies in China’s manufacturing industry from 2016 to 2020, based on a two-way fixed effects model. The paper divides technological innovation into two dimensions to explore the impact of breakthrough innovation and incremental innovation on enterprises’ capacity utilization. The results show that both breakthrough and incremental innovations can effectively improve the capacity utilization of enterprises; however, the breakthrough innovation has a greater degree of improvement. The moderating effect shows that a good institutional environment strengthens the positive relationship between breakthrough innovations and the capacity utilization of enterprises. The heterogeneity analysis finds that breakthrough and incremental innovations have a stronger effect on the capacity utilization of state-owned enterprises. This paper further improves the theoretical framework of the relationship between technological innovation and enterprise capacity utilization and provides a theoretical basis and motivation for the Chinese manufacturing industry to resolve overcapacity and respond to the national requirements for high-quality development.
Assessment of Outdoor Design Conditions on the Energy Performance of Cooling Systems in Future Climate Scenarios—A Case Study over Three Cities of Texas, Unites States
Authors: Karimi Alireza , Kim You Joung , Zadeh Negar Mohammad , García-Martínez Antonio , Delfani Shahram , Brown Robert D. , Moreno-Rangel David , Mohammad Pir
Published: November 2022

The excessive use of energy in buildings due to increased populations and economic development leads to more greenhouse gas emissions, which affect climate change and global warming. Changes in prevailing outdoor weather conditions significantly affect the energy systems of buildings through increased cooling and decreased heating. In this paper, 30 years of data of dry and wet bulb temperatures (1990–2020) with a time interval of 3 h were considered in order to estimate the climatic outdoor design conditions in the cities of Dallas–Fort Worth, Houston, and San Antonio in the state of Texas. The results suggest that the dry bulb temperature (DBT) had significantly higher increases in Dallas–Fort Worth (2.37 °C) than the wet bulb temperature (WBT) in Houston (4.1 °C) during the study period. Furthermore, this study analyzed the effects of climate change on cooling degree hours (CDH) and heating degree hours (HDH) and the results suggest the most significant drop in HDH in Dallas–Fort Worth with a maximum CDH fluctuation as compared to other two cities. The effect of climate change on the performance of cooling systems is also investigated in this study via direct evaporative coolers (DECs) and direct-indirect evaporative coolers (IDEC), which do not perform well in the selected cities. In contrast, absorption system (Abs) and vapor compression (VC) systems show an increase in the number of additional loads. The second part of this study is related to the future projection using the ARIMA model, which suggests that DBT would rise significantly in Houston (from 37.18 °C to 37.56 °C) and Dallas–Fort Worth (39.1 °C to 39.57 °C) while diminishing in San Antonio (from 34.81 °C to 33.95 °C) from 2020 to 2030. In contrast, WBT will experience an upward trend in Houston (from 36.06 °C to 37.71 °C) and Dallas–Fort Worth (from 31.32 °C to 31.38 °C) and a downward trend in San Antonio (from 32.43 °C to 31.97 °C) during 2020–2030. Additionally, the future performance prediction of Abs and VC systems is also performed, which reveals that the amount of additional load required is significantly higher in 2030 compared to 2020 and is more prominent in Houston. Conversely, amount of additional load required for cooling systems in San Antonio shows a decreasing trend in 2030.
Litigation Risk and Corporate Social Responsibility—Evidence from a Poverty Alleviation Campaign in China
Authors: Jing Jielin , Wang Jianling , Wu Qingjun
Published: November 2022

This paper investigates the impact of an external uncertain factor, litigation risk, on corporate participation in Targeted Poverty Alleviation (TPA) activities. It proposes and explores three possible mechanisms, namely, restore legitimacy, send positive signals, and maintain corporate reputation, for corporations to manage their litigation risk via the participation in TPA. Using a sample of Chinese listed firms from 2016 to 2020, it shows that for corporations with high legitimacy pressure, high stakeholder concern, and strong reputation protection motive, litigation risk increases corporate investment in TPA. After litigation cases arise, corporations can manage their litigation risk through participation in TPA, thereby restoring legitimacy, sending positive signals, and maintaining corporate reputation. Furthermore, participation in TPA can also moderate the negative impact of litigation risk on enterprise value. The results remain significant after robustness tests on endogeneity, variable and measurement errors, and firm fixed effects. This paper is insightful for future studies relating to the economic consequences of litigation risk. Concurrently, by exploring the role of China’s legal environment in promoting the effect of corporate participation in TPA, this paper not only expands the scope of factors influencing corporate TPA inputs, but also provides policy implications for the formulation of China’s upcoming Rural Revitalization Strategy.
Sulfur Sources Mediated the Growth, Productivity, and Nutrient Acquisition Ability of Pearlmillet–Mustard Cropping Systems
Authors: Rathore Sanjay Singh , Babu Subhash , Singh Vinod Kumar , Shekhawat Kapila , Singh Rajiv Kumar , Upadhyay Pravin Kumar , Hashim Mohammad , Sharma K. C. , Jangir Rameti , Singh Raghavendra
Published: November 2022

Globally, excess soil nutrient mining is a serious challenge to sustainable agricultural production. The task is more daunting in the semi-arid region of the globe. In addition to the primary nutrient deficiency over the mining of secondary nutrients, especially sulfur is an emerging challenge in contemporary crop production systems. Hence, there is a dire need to devise an appropriate sulfur management protocol by searching for efficient and sustainable sulfur sources. Therefore, the efficacy of new sulfur molecules was evaluated on the performance and nutrient acquisition potential of the pearl millet–mustard system at the research farm of the Indian Agricultural Research Institute in New Delhi, India, for two years. The flexibility of urea–ES technology allows customized sulfur-enhanced urea formulations that suit unique crop needs, offering an all-in-one nitrogen and sulfur fertilizer solution. Hence, the study hypothesized that new sulfur molecules like sulfonated urea (SEU) will have a positive impact on crop growth, yield, and nutrient acquisition in the pearl millet–mustard system. The results revealed that the application of 50% sulfur (S) (15 kg/ha) as a base and 50% (15 kg/ha) as a topdressing (SEU 10-0-0-75) produced better growth, yield-contributing parameters, and economic productivity of the pearl millet–mustard system. However, both compositions of SEU (40-0-0-13 and 10-0-0-75) were significantly better than the recommended dose of fertilizer (RDF) and the RDF along with other S sources like single super phosphate and bentonite in enhancing crop growth and productivity. The agronomic efficiency of nitrogen (AEn) and S (Aes) of SEU (40-0-0-13 and 10-0-0-75) were 9.1 and 10.3 kg seed yield/kg N and 51.2 and 28.9 kg seed/kg, respectively. The agronomic nitrogen use efficiency of SEU (40-0-0-13) and SEU (10-0-0-75) is significantly higher than those of nitrogen, phosphorus, and potassium (NPK) alone. Thus, the findings inferred that splitting S (50% as a base and 50% as topdressing) through SEU is a practically feasible and economically robust S option for profitable and sustainable production of the pearl millet–mustard production model in the semi-arid region.