The evolution of carbon footprint in the yangtze river delta city cluster during economic transition 2012-2015

China has been undergoing an industrial transformation, shifting from an energy-intensive growth pattern. As the most developed region in China, the Yangtze River Delta (YRD) city cluster is leading the industrial trans- formation. However, the impact of the industrial transformation on carbon footprints in the YRD cities is unclear. By a city-level environmentally extended input-output model, we quantify the carbon footprint of 41 cities in the YRD city cluster for 2012 and 2015 and capture the socioeconomic driving forces of the change by structural decomposition analysis (SDA). The results show that the carbon footprint in 41 YRD cities increased from 1179.4 Mt (14.8% of China’s total) to 1329.6 Mt (16.6%) over the period. More than 60% of the footprint concentrated on the 10 largest cities, and the construction sector made the largest contribution, especially in service-based megacities. The change of production structure drove down carbon footprints in YRD cities, except light in- dustry cities and service-based cities. The industrial transfers from the coastal to inland regions result in carbon leakage, where one-third of the carbon footprint is embodied in the trade. We also find the economic recession during the transition period decreased carbon emissions by 154.2 Mt in the YRD city cluster, where the value- added rate in the YRD cities declined over the transition period, especially in service-based cities. The study highlights the positive effects of industrial transformation on low carbon transition, despite being highly heterogenous for cities

Outsourced carbon mitigation efforts of Chinese cities from 2012 to 2017

Outsourced carbon mitigation between cities means that some cities benefit from the carbon mitigation efforts of other cities more than their own. This problem conceals the recognition of cities’ mitigation contributions. Here we quantify local and outsourced carbon mitigation levels from 2012 to 2017 and identified ‘outsourced mitigation beneficiaries’ relying on outsourced efforts more than their own among 309 Chinese cities by using a city-level input–output model. It found that the share of outsourced emissions rose from 78.6% to 81.9% during this period. In particular, 240 cities (77.7%) were outsourced mitigation beneficiaries, of which 65 were strong beneficiaries (their local carbon emissions still grew) and 175 cities were weak beneficiaries (with larger outsourced mitigation efforts than local mitigation efforts). Strong beneficiaries were often industrializing cities with more agriculture and light manufacturing, focusing on local economic growth. In contrast, weak beneficiaries were mainly at the downstream of supply chains with services and high-tech manufacturing, which have stronger connections with upstream heavy industry cities. The findings suggest the need for policies to manage outsourced mitigation of supply chains and encourage transformation, improving the fair acknowledgment of cities’ carbon mitigation efforts

Community structure and plant diversity under different degrees of restored grassland in mining areas of the Qilian Mountains, Northwestern China

Background: Mining activities are known to exert significant effects on the structure and function of grassland ecosystems. However, the role of mining grasslands restoration in altering the plant community and soil quality remains poorly understood, especially in alpine regions. Here, we investigated species diversity in grasslands with dynamic changes and different restoration levels in the Tianzhu alpine mining area locating in the Qilian Mountains.Methods: The plant community structure and species composition of the grasslands with different restoration levels were analyzed by the sample method. We used five different restoration levels: very low recovered degree (VLRD), low recovered degree (LRD), medium recovered degree (MRD), and high recovered degree (HRD), and selected natural grassland (NGL, CK) as the control.Results: Plant community structure and species composition were significantly higher than those under the VLRD in the Tianzhu alpine mining area (p < 0.05), with HRD > MRD > LRD > VLRD. There were 11 families, 18 genera, and 17 species of plants, mainly in the families of Leguminosae, Asteraceae, Gramineae, Rosaceae, and Salicaceae; among them, Salicaceae and Gramineae played a decisive role in the stability of the community. The ecotype community showed that perennial herbaceous plants were the most dominant, with annual herbaceous plants being the least dominant, and no tree and shrub layers were observed; the dominance index was the highest in VLRD at 0.32, the richness index was the highest in HRD at 2.73, the diversity of HRD was higher at 1.93, soil pH and EC showed a decreasing trend, and SMC, SOC, TN, NO3-N, NH4-N, AN, TP, and AP content showed an increasing trend with the increase of grassland restoration.Conclusion: In summary, with the increase of restored grassland in the Tianzhu alpine mining area, plant diversity gradually increased and plant community structure gradually diversified, which was close to the plant diversity of NGL. The protection of partially VLRD and LRD grasslands in the mining area should be emphasized, and the mine grassland should be used rationally and scientifically restored

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001.Peer reviewe

The evolution of carbon footprint in the yangtze river delta city cluster during economic transition 2012-2015

China has been undergoing an industrial transformation, shifting from an energy-intensive growth pattern. Asthe most developed region in China, the Yangtze River Delta (YRD) city cluster is leading the industrial trans-formation. However, the impact of the industrial transformation on carbon footprints in the YRD cities is unclear.By a city-level environmentally extended input-output model, we quantify the carbon footprint of 41 cities in theYRD city cluster for 2012 and 2015 and capture the socioeconomic driving forces of the change by structuraldecomposition analysis (SDA). The results show that the carbon footprint in 41 YRD cities increased from 1179.4Mt (14.8% of China’s total) to 1329.6 Mt (16.6%) over the period. More than 60% of the footprint concentratedon the 10 largest cities, and the construction sector made the largest contribution, especially in service-basedmegacities. The change of production structure drove down carbon footprints in YRD cities, except light in-dustry cities and service-based cities. The industrial transfers from the coastal to inland regions result in carbonleakage, where one-third of the carbon footprint is embodied in the trade. We also find the economic recessionduring the transition period decreased carbon emissions by 154.2 Mt in the YRD city cluster, where the value-added rate in the YRD cities declined over the transition period, especially in service-based cities. The studyhighlights the positive effects of industrial transformation on low carbon transition, despite being highlyheterogenous for cities

China’s low-carbon policy intensity dataset from national- to prefecture-level over 2007–2022

Abstract Low-carbon policies are essential for facilitating manufacturing industries’ low-carbon transformation and achieving carbon neutrality in China. However, recent studies usually apply proxy variables to quantify policies, while composite indices of policy intensity measured by objectives and instruments focus more on the national level. It is deficient in direct and comprehensive quantification for low-carbon policies. Hence, having extended the meaning of policy intensity, this paper constructs a low-carbon policy intensity index quantified by policy level, objective and instrument via phrase-oriented NLP algorithm and text-based prompt learning. This process is based on the low-carbon policy inventory we built for China’s manufacturing industries containing 7282 national-, provincial- and prefecture-level policies over 2007–2022. Lastly, we organize the dataset in two formats (.dta and .xlsx) for multidiscipline researchers. Apart from the inventory and intensity for each policy, the policy intensity is also aggregated to national-, provincial- and prefecture-level with sub-intensity for four objectives and three instruments. This dataset has potential uses for future studies by merging with macro and micro data related to low-carbon performances

Effects of pulsed electromagnetic fields on the mRNA expression of RANKL and OPG in ovariectomized rat osteoblast-like cell

Conference Name:2014 International Conference on Medical Engineering and Bioinformatics, MEB 2014. Conference Address: Wuhan, China. Time:April 7, 2014 - April 8, 2014.Information Technology and Industrial Engineering Research CenterThis study was designed to determine the effects of pulsed electromagnetic fields (PEMFs) on the mRNA expression of the receptor activator of NF-_B ligand (RANKL) and osteoprotegerin (OPG) in ovariectomized rat osteoblast-like cells. Osteoblast-like cells were harvested from calvariae of rats, from which the ovaries had been totally excised. The third passaged osteoblast-like cells were used in the present experiments. Osteoblast-like cells were exposed to PEMFs for 5 days with 3.8 mT, 8 Hz, and 40 min per day. The expression of RANKL and OPG mRNA was determined with real-time fluorescent quantitative polymerase chain reaction. Compared with the sham group, the level of serum estradiol in the ovariectomized group was significantly decreased (P<0.05). Compared with the ovariectomized experimental (PEMFs) group and sham group, RANKL mRNA expression was significantly increased in the ovariectomized group (P<0.05, P<0.01, respectively). These data suggest that PEMFs could regulate the expression of RANKL mRNA of osteoblast-like cells

Corrigendum to <'The evolution of carbon footprint in the yangtze river delta city cluster during economic transition 2012–2015′>

Resources, Conservation & Recycling 181 (2022) 106,266 The authors regret to inform the readers of an error in a figure and the Supporting Information not uploaded. The color of legends of “Carbon intensity per GDP” and “Value-added rate” in Fig. 5 are reversed. Here is a corrected version of Fig. 5 (only the color of legends of “Carbon intensity per GDP” and “Value-added rate” in Fig. 5 have been modified): The authors regret . The authors would like to apologise for any inconvenience caused. DOI of original article: < https://doi.org/10.1016/j.resconrec.2022.106266

Influence of Bionic Circular Groove Blade Surface on Wear Performance

In order to improve the anti-wear performance of a double-vane self-priming centrifugal pump during two-phase flow transfer, the construction of a streamline groove structure at the outlet end of the suction side of the vane, based on the bionic principle, is proposed. Different pump characteristics are analysed to investigate the effect of different bionic groove spacing on the resistance to particle wear and the mechanism of improvement of the bionic grooves. The results show that the effect of the bionic blades on the hydraulic characteristics of the pump is within the allowable error (±1.4%). The circular groove structure with different spacing produces a difference in the pressure distribution on the blade. At the same particle concentration, with the increase in the groove spacing distance, the average wear of the blades first decreases and then increases; the average wear rate at the spacing of 7 mm is the smallest. At a particle concentration of 90 kg/m3, the average wear rate at a groove spacing of 7 mm is ~0.63 × 10−4 kg/s∙m2, and the wear area is mainly found in the middle of the blade. The reason why the bionic blade improves the anti-wear performance of the pump is due to the reverse vortex zone in the groove, which changes the particle trajectory and collision frequency. The bionic grooves with a diameter of 2 mm and a spacing of 7 mm significantly reduce the average wear rate of the pump at different particle concentrations, while maintaining hydraulic performance