Mobile phone network data reveal nationwide economic value of coastal tourism under climate change
ارزش اقتصادی داده های شبکه تلفن همراه در سراسر جهان از گردشگری ساحلی در اثر تغییر آب و هوا-2020
The technology-driven application of big data is expected to assist policymaking towards sustainable development; however, the relevant literature has not addressed human welfare under climate change, which limits the understanding of climate change impacts on human societies. We present the first application of unique mobile phone network data to evaluate the current nation-wide human welfare of coastal tourism at Japanese beaches and project the value change using the four climate change scenarios. The results show that the projected national economic value loss rates are more significant than the projected national physical beach loss rates. Our findings demonstrate regional differences in recreational values: most southern beaches with larger current values would disappear, while the current small values of the northern beaches would remain. These changes imply that the ranks of the beaches, based on economic values, would enable policymakers to discuss management priorities under climate change.
Keywords: Adaptation | Beach recreation | Big data | Climate change | Coastal tourism | Ecosystem services | Travel cost method | Sea level rise
An empirical case study on Indian consumers sentiment towards electric vehicles: A big data analytics approach
یک مطالعه موردی تجربی در مورد احساسات مصرف کنندگان هندی نسبت به وسایل نقلیه برقی: یک رویکرد تحلیل داده های بزرگ-2020
Today, climate change due to global warming is a significant concern to all of us. Indias rate of greenhouse gas emissions is increasing day by day, placing India in the top ten emitters in the world. Air pollution is one of the significant contributors to the greenhouse effect. Transportation contributes about 10% of the air pollution in India. The Indian government is taking steps to reduce air pollution by encouraging the use of electric vehicles. But, success depends on consumers sentiment, perception and understanding towards Electric Vehicles (EV). This case study tried to capture the feeling, attitude, and emotions of Indian consumers towards electric vehicles. The main objective of this study was to extract opinions valuable to prospective buyers (to know what is best for them), marketers (for determining what features should be advertised) and manufacturers (for deciding what features should be improved) using Deep Learning techniques (e.g Doc2Vec Algorithm, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN)). Due to the very nature of social media data, big data platform was chosen to analyze the sentiment towards EV. Deep Learning based techniques were preferred over traditional machine learning algorithms (Support Vector Machine, Logistic regression and Decision tree, etc.) due to its superior text mining capabilities. Two years data (2016 to 2018) were collected from different social media platform for this case study. The results showed the efficiency of deep learning algorithms and found CNN yield better results in-compare to others. The proposed optimal model will help consumers, designers and manufacturers in their decision-making capabilities to choose, design and manufacture EV.
Keywords: Electric vehicles | Deep learning | Big data | Sentiment analysis | India
انتشار چهارچوب حسابداری برای پارک های صنعتی در چین
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 12 - تعداد صفحات فایل doc فارسی: 31
چین بیشترین تعداد پارک های صنعتی را در جهان دارد. این پارکها نه تنها برای تسریع بخشیدن فرایند صنعتی کشور مهم و حیاتی بلکه برای دست یافتن به اهداف تغیرات آب و هوایی خود نیزمهم و حیاتی هستند. ایجاد فهرست انتشار CO2برای پارک صنعتی اولین مرحله در تحلیل الگوهای انتشار پارک و طراحی سیاست های کم کربن می باشد. به هرحال، بیشتر انتشار قبلی برای پارک های صنعتی با اتخاذ حوزه و روش شناسی مختلف محاسبه می شود که با یکدیگر قابل مقایسه نیستند. این مطالعه روش شناسی و چارچوب خودسازگاری را برای پارک های صنعتی چین مبتنی بر داده سطح شرکت توسعه می دهد. ما هر دو حوزه انتشار 1 و2 را بررسی و فهرست ها را با 19 نوع انرژی و 39 بخش صنعتی ایجاد می کنیم که سازگار با فهرست های انتشار از سطح شهر، استانی و کشوری می باشد. چنین فهرست انتشار مبتنی بر بخش نه تنها قادرخواهد بود تا داده های حمایتی برای طراحی سیاستهای کنترل انرژی/انتشار ارائه دهد بلکه به دولت محلی/مرکزی جهت ارزیابی عملکرد کاهش انتشار پارک کمک می کند. سرانجام، مطالعه تجربی برای چهار پارک صنعتی برای تایید این روش اجرا می شود. علاوه براین، ما برنامه های پارک اکو-صنعتی را در کشورهای ژاپن، کره جنوبی و همچنین ساختار حسابداری انتشاری آنها را بررسی می کنیم. متوجه شدیم که بیشتر پارک های صنعتی ژاپن انتشارهایی با حوزه 1،2 و 3 ارائه می دهند درحالیکه برای کره جنوبی، پارک ها اکثرا در انتشار حوزه 1 تمرکز می کنند. بحث اکو-صنعتی پارک های ژاپن و کره جنوبی اهمیت قابل توجهی برای ساخت پارک های کم – کربن چین دارد.
کلمات کلیدی: انتشار CO2 | پارک های صنعتی | تغییرات آب و هوا | چین
|مقاله ترجمه شده|
Using big data to improve ecotype matching for Magnolias in urban forestry
استفاده از داده های بزرگ برای بهبود تطابق اکوتیپ برای ماگنولیاها در جنگل های شهری-2020
Trees play major roles in many aspects of urban life, supporting ecosystems, regulating temperature and soil hydrology, and even affecting human health. At the scale of the urban forest, the qualities of these individual trees become powerful tools for mitigating the effects of, and adapting to climate change and for this reason attempts to select the right tree for the right place has been a long-term research field. To date, most urban forestry practitioners rely upon specialist horticultural texts (the heuristic literature) to inform species selection whilst the majority of research is grounded in trait-based investigations into plant physiology (the experimental literature). However, both of these literature types have shortcomings: the experimental literature only addresses a small proportion of the plants that practitioners might be interested in whilst the data in the heuristic (obtained through practice) literature tends to be either too general or inconsistent. To overcome these problems we used big datasets of species distribution and climate (which we term the observational literature) in a case study genus to examine the climatic niches that species occupy in their natural range. We found that contrary to reports in the heuristic literature, Magnolia species vary significantly in their climatic adaptations, occupying specific niches that are constrained by trade-offs between water availability and energy. The results show that not only is ecotype matching between naturally-distributed populations and urban environments possible but that it may be more powerful and faster than traditional research. We anticipate that our findings could be used to rapidly screen the world’s woody flora and rapidly communicate evidence to nurseries and plant specifiers. Furthermore this research improves the potential for urban forests to contribute to global environmental challenges such as species migration and ex-situ conservation.
Keywords: Big data | Biogeography | Ecotype matching | Predictive ecology | Urban trees
Tweeting the United Nations Climate Change Conference in Paris (COP21): An analysis of a social network and factors determining the network influence
توییت کنفرانس تغییرات اقلیمی سازمان ملل متحد در پاریس (COP21): تحلیلی از یک شبکه اجتماعی و عوامل تعیین کننده تأثیر شبکه-2020
To understand the Twitter network of an environmental and political event and to extend the network theory of social capital, we first performed a network analysis of the English tweets during the first 10 days of the United Nations’ Conference of the Parties in Paris in 2015. Accounts for nonprofit and government agencies were more likely to be influential in the Twitter network and be retweeted, whereas individual accounts were more likely to retweet others. Based on a quota sample of 133 Twitter accounts and using both manual and machine coding, we further found that the number of followers (but not the size of following) and the common-goal frame (i.e., mitigation/adaptation) positively predicted an account’s influence in the Twitter network, whereas the conflict frame negatively predicted an account’s network influence
Keywords: Big data | Climate change | COP21 | Framing | Social capital | Social network analysis
Estimating monthly wet sulfur (S) deposition flux over China using an ensemble model of improved machine learning and geostatistical approach
برآورد شار رسوب ماهانه گوگرد مرطوب (S) بر روی چین با استفاده از مدل گروهی از یادگیری ماشین پیشرفته و روش زمین آماری-2019
The wet S deposition was treated as a key issue because it played the negative on the soil acidification, biodiversity loss, and global climate change. However, the limited ground-level monitoring sites make it difficult to fully clarify the spatiotemporal variations of wet S deposition over China. Therefore, an ensemble model of improved machine learning and geostatistical method named fruit fly optimization algorithm-random forestspatiotemporal Kriging (FOA-RF-STK) model was developed to estimate the nationwide S deposition based on the emission inventory, meteorological factors, and other geographical covariates. The ensemble model can capture the relationship between predictors and S deposition flux with the better performance (R2=0.68, root mean square error (RMSE)=7.51 kg ha−1 yr−1) compared with the original RF model (R2=0.52, RMSE=8.99 kg ha−1 yr−1). Based on the improved model, it predicted that the highest and lowest S deposition flux were mainly concentrated on the Southeast China (69.57 kg S ha−1 yr−1) and Inner Mongolia (42.37 kg S ha−1 yr−1), respectively. The estimated wet S deposition flux displayed the remarkably seasonal variation with the highest value in summer (22.22 kg S ha−1 sea−1), follwed by ones in autumn (18.30 kg S ha−1 sea−1), spring (16.27 kg S ha−1 sea−1), and the lowest one in winter (14.71 kg S ha−1 sea−1), which was closely associated with the rainfall amounts. The study provides a novel approach for the S deposition estimation at a national scale.
Keywords: Wet S deposition | Machine learning | Geostatistical approach | China
Conservation of data deficient species under multiple threats: Lessons from an iconic tropical butterfly (Teinopalpus aureus)
حفاظت از گونه های کمبود داده در معرض تهدیدات متعدد: درسهایی از یک پروانه گرمسیری نمادین (Teinopalpus aureus)-2019
With increasing pressure from wildlife trade, conservation eﬀorts must balance deﬁciencies in distribution data for species (the Wallacean shortfall) with the risk of increasing accessibility of locality for collectors. The Golden Kaiser-I-Hind (Teinopalpus aureus Mell) is an iconic butterﬂy restricted to Southeast Asia, popular in trade markets but lacking in ecological and conservation information. We compiled occurrence records and used them to assess multiple threats of T. aureus distribution-wide and at the national level. Results of species distribution models suggest that suitable habitats of T. aureus are montane forests in mid to high elevations in Southern China, Laos and Vietnam. However, habitat networks for the species are poorly connected, with some portions of its distribution experiencing intensive deforestation and threatened by climate change. The trade assessment results showed specimens of T. aureus were available for sale with high prices, indicating potential pressure from trade markets. We also found diﬀerent conservation statuses and eﬀorts to protect T. aureus across countries; the species is under strict protection in China, moderate protection in Vietnam and has no protection in Laos. Both recorded locations and projected distribution in the three countries were poorly covered by protected areas. These results together demonstrate the importance of distribution data in conservation management of threa- tened species while highlighting trade-oﬀs inherent in not making location information widely available when trade pressure is present. Finally, we strongly encourage cross-border cooperation in sharing ecological in- formation for consistent conservation management of species under multiple threats from habitat loss, climate change and illegal wildlife trade.
Keywords: Climate change | Cross-border conservation | Habitat loss | Insect conservation | Southeast Asia | Wildlife trade
Using machine learning to quantify the impacts of genetically modified crops on US midwest corn yields
استفاده از یادگیری ماشینی برای تعیین کمیت تأثیر محصولات اصلاح شده ژنتیکی بر عملکرد ذرت میان غربی ایالات متحده-2019
Global food security is becoming increasingly stressed by growing populations and climate change. To compensate for these stresses, crop yields must increase throughout the upcoming century. One of the more prominently featured solutions entails genetically modified crops, but their impacts on yields are contested. Here, we leverage machine learning techniques to examine the effects genetically modified crops have had on US corn yields. In particular, a principal components analysis conducted on US Midwest county yields reveals that the commercialization of genetically modified corn accentuated preexisting spatial disparities in production and explains approximately 6–12% of the regions inter-county variation in yields from 1980 to 2015. Additionally, counterfactual yield trajectories predicted by Bayesian structural time series models using non-genetically modified crops as synthetic controls suggest that the adoption of this biotechnology amounted to an approximate 13% increase in overall US corn yields from 1996 to 2015.
Keywords: GM crops | Principal components analysis | Corn yields | Machine learning | Bayesian structural time series
Detecting temporal changes in the temperature sensitivity of spring phenology with global warming: Application of machine learning in phenological mode
تشخیص تغییرات زمانی در حساسیت دما به فنولوژی فنر با گرم شدن کره زمین: کاربرد یادگیری ماشین در حالت فنولوژی-2019
Phenological models can effectively infer historically missing phenological data, so as to investigate the longterm relationship between plants and climate change. Large numbers of ecophysiological and statistical models have been developed in the past few decades, but these models have been unable to make accurate predictions based on external data. Machine learning (ML) methods have an advantage over traditional statistical methods for natural science studies. However, only a few phenological models have been coupled with ML methods. In this study, using long-term leaf unfolding date (LUD) observations collected in Harbin, China, we adopted three popular ML algorithms for predicting plant LUD and compared the performances of 10 phenological models. We detected the temperature sensitivity (ST) of the LUD at the species level for the periods 1962–1987 and 1988–2016 (before and after the recent, sudden warming) and temporal changes in ST with a 15-year moving window for each period. The results show that the gradient boosting decision tree (GBDT) model performs obviously better than the other models for external validation data, while avoiding model overfitting. Most species showed an increase in ST during the 1988–2016 period, and the temporal changes in ST significantly decreased during both periods. The temporal changes in ST from the phenological data predicted by the GBDT model is significantly higher than that of other models, which indicates that the traditional phenological models may underestimate the response of LUD to climate warming. We found a prevalent decline in the magnitude of ST with increasing preseason temperature variance at the species level. Our research suggests that machine learning algorithms should be more widely used in future phenological model research, and temporal changes in ST should be investigated in order to broaden our understanding of plants’ ability to adapt to future climate change.
Keywords: Leaf unfolding date | Machine learning | Phenological model | Temperature sensitivity | Temperature variance | Temporal changes
Construction of a drought monitoring model using deep learning based on multi-source remote sensing data
ساخت مدل مانیتورینگ خشکسالی با استفاده از یادگیری عمیق بر اساس داده های سنجش از دور چند منبعی-2019
Drought is a popular scientific issue in global climate change research. Accurate monitoring of drought has important implications for the sustainable development of regional agriculture in the context of increasingly complex global climate change. Deep learning is a widely used technique in the field of artificial intelligence. However, ongoing on drought monitoring using deep learning is relatively scarce. In this paper, the various hazard factors in drought development were comprehensively considered based on satellite data including Moderate Resolution Imaging Spectroradiometer (MODIS) and tropical rainfall measuring mission (TRMM) as multi-source remote sensing data. By using the deep learning technique, a comprehensive drought monitoring model was constructed and tested in Henan Province of China as an example. The results showed that the comprehensive drought model has good applicability in the monitoring of meteorological drought and agricultural drought. There was a significant positive correlation between the drought indicators of the model output and the comprehensive meteorological drought index (CI) measured at the site scale. The consistency rate of the drought grade of the two models was 85.6% and 79.8% for the training set and the test set, respectively. The correlation coefficient between the drought index of the model and the standard precipitation evapotranspiration index (SPEI) was between 0.772 and 0.910 (P < 0.01), which indicated a strong level of significance. The correlation coefficient between the drought index of the model and the soil relative moisture at a 10 cm depth was greater than 0.550 (P < 0.01), and there was a good correlation between them. This study provides a new method for the comprehensive assessment of regional drought.
Keywords: Drought | Remote sensing | Deep learning