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This paper provides economic estimates of the energy-related climate damages of mining Bitcoin (BTC), the dominant proof-of-work cryptocurrency. We provide three sustainability criteria for signaling when the climate damages may be unsustainable. BTC mining fails all three. We find that for 2016-2021: (i) per coin climate damages from BTC were increasing, rather than decreasing with industry maturation; (ii) during certain time periods, BTC climate damages exceed the price of each coin created; (iii) on average, each $1 in BTC market value created was responsible for $0.35 in global climate damages, which as a share of market value is in the range between beef production and crude oil burned as gasoline, and an order-of-magnitude higher than wind and solar power. Taken together, these results represent a set of sustainability red flags. While proponents have offered BTC as representing "digital gold," from a climate damages perspective it operates more like "digital crude"., (© 2022. The Author(s).)

Linking environmental, socioeconomic and health datasets provides new insights into the potential associations between climate change and human health and wellbeing, and underpins the development of decision support tools that will promote resilience to climate change, and thus enable more effective adaptation. This paper outlines the challenges and opportunities presented by advances in data collection, storage, analysis, and access, particularly focusing on "data mashups". These data mashups are integrations of different types and sources of data, frequently using open application programming interfaces and data sources, to produce enriched results that were not necessarily the original reason for assembling the raw source data. As an illustration of this potential, this paper describes a recently funded initiative to create such a facility in the UK for use in decision support around climate change and health, and provides examples of suitable sources of data and the purposes to which they can be directed, particularly for policy makers and public health decision makers.

Big climate change data have become a pressing issue that organizations face with methods to analyze data generated from various data types. Moreover, storage, processing, and analysis of data generated from climate change activities are becoming very massive, and are challenging for the current algorithms to handle. Therefore, big data analytics methods are designed for significantly large amounts of data required to enhance seasonal change monitoring and understand and ascertain the health risks of climate change. In addition, analysis of climate change data would improve the allocation, and utilisation of natural resources. This paper provides an extensive discussion of big data analytic methods for climate data analysis and investigates how climate change and sustainability issues can be analyzed through these approaches. We further present the big data analytic methods, strengths, and weaknesses, and the essence of analyzing big climate change using these methods. The common datasets, implementation frameworks for climate change modeling, and future research directions were also presented to enhance the clarity of these compelling climate change analysis challenges. This big data analytics method is well-timed to solve the inherent issues of data analysis and easy realization of sustainable development goals. [ABSTRACT FROM AUTHOR]

Climate change, pollution, and deforestation have a negative impact on global mental health. There is an environmental justice dimension to this challenge as wealthy people and high‐income countries are major contributors to climate change and pollution, while poor people and low‐income countries are heavily affected by the consequences. Using state‐of‐the art data mining, we analyzed and visualized the global research landscape on mental health, climate change, pollution and deforestation over a 15‐year period. Metadata of papers were exported from PubMed®, and both relevance and relatedness of terms in different time frames were computed using VOSviewer. Co‐occurrence graphs were used to visualize results. The development of exemplary terms over time was plotted separately. The number of research papers on mental health and environmental challenges is growing in a linear fashion. Major topics are climate change, chemical pollution, including psychiatric medication in wastewater, and neurobiological effects. Research on specific psychiatric syndromes and diseases, particularly on their ethical and social aspects is less prominent. There is a growing body of research literature on links between mental health, climate change, pollution, and deforestation. This research provides a graphic overview to mental healthcare professionals and political stakeholders. Social and ethical aspects of the climate change‐mental health link have been neglected, and more research is needed. Plain Language Summary: Climate change, deforestation, and pollution are having a major effect on mental health all around the world. Yet there are huge disparities on how these negative consequences affect people within and between countries. We analyzed large databases of research articles using digital tools (data mining) to uncover the direction of scientific research and areas that have received less scholarly attention. While research linking climate change to mental health issues is expanding, a detailed examination of the social justice dimension of how climate change and pollution are affecting the different groups of people is still relatively scarce. We provide a graphical overview of the most important research keywords of the last 15 years. Key Points: Climate change, pollution, and deforestation threaten global mental health and need to be addressed as a mental health issueData mining can help to uncover trends and gaps in researchMental health research on climate change and pollution is growing, while research linking these to environmental injustice is less prominent [ABSTRACT FROM AUTHOR]

Indigenous knowledge about climate change that makes adaptability necessary by coping strategies leads to the sustainability of nomadic livelihoods. This paper proposed a new adaptability approach, an association coping strategy (ACS), which is established based on the association rulesʼ recommendations. The adaptability was empirically analyzed using 12 coping strategies used by Kashkooli nomads in Sepidan plain, Fars province, Iran. Required data were gathered both in the form of time series from November 1988 to February 2020 and cross section during June–July 2019. Household perceptions of climatic and environmental variables, supported by meteorological data, revealed that climate change was negatively impacting the Kashkooli nomadic livelihood in Sepidan plain. The findings also indicated that taking simultaneous coping strategies resulted in selling livestock surplus, saving water consumption, livestock insurance, rangeland regeneration schemes, and immigration to more suitable rangelands. Therefore, Kashkooli nomadic livelihood survived climate changes using ACSs. The association rules were useful for recommending anticipatory coping strategies that were adopted based on indigenous knowledge about climate change by nomads to establish appropriate adaptability. [ABSTRACT FROM AUTHOR]

The growing concern over catastrophic weather events, mostly as a direct result of climate changes, has underscored the need for expanding traditional power system contingency analyses to handle the associated risks of extreme power outages. To enable power system operators to make timely decisions when facing extreme events, we explore in this paper the viability of a classifier which uses the machine learning approach based on the Bayes decision theory as a means of predicting power system component outages. However, owing to an excessively imbalanced and largely sparse power component outage datasets, the corresponding classifier learning is a challenging problem in the data mining community. In the proposed approach, we apply a resampling method to overcome the class imbalance problem. The proposed classifier provides an effective framework that not only minimizes outage prediction errors for power system components, but also considers the cost of each preventive action according to its implication in extreme events. The outcome of the proposed model can be used for introducing operation-oriented preventive measures that allow the rescheduling of generation resources for maximizing the power system resilience. The performance of the proposed classifier is examined through numerical simulations by utilizing the confusion matrix. [ABSTRACT FROM AUTHOR]

For more than 40 years, Earth observation satellites have been regularly providing images of glaciers that can be used to derive surface displacement fields and study their dynamics. In the context of global warming, the analysis ofdisplacement field time series (DFTS)can provide useful information. Efficient data mining techniques are, thus, required to extract meaningful displacement evolutions from such large and complex datasets. In this paper, a pattern-based data mining approach, which handles confidence measures, is proposed to analyze DFTS. In order to focus on the most reliable measurements, a displacement evolution reliability measure is defined. It is aimed at assessing the quality of each evolution and pruning the search space. Experiments on two different DFTS (annual displacement fields derived from optical data over Greenland ice sheet and 11-day displacement fields derived from synthetic aperture radar data over Alpine glaciers) show the potential of the proposed approach. [ABSTRACT FROM AUTHOR]

In agriculture sector where farmers and agribusinesses have to make innumerable decisions every day and intricate complexities involves the various factors influencing them. An essential issue for agricultural planning intention is the accurate yield estimation for the numerous crops involved in the planning. Data mining techniques are necessary approach for accomplishing practical and effective solutions for this problem. Agriculture has been an obvious target for big data. Environmental conditions, variability in soil, input levels, combinations and commodity prices have made it all the more relevant for farmers to use information and get help to make critical farming decisions. This paper focuses on the analysis of the agriculture data and finding optimal parameters to maximize the crop production using data mining techniques like PAM, CLARA, DBSCAN and Multiple Linear Regression. Mining the large amount of existing crop, soil and climatic data, and analysing new, non-experimental data optimizes the production and makes agriculture more resilient to climatic change. [ABSTRACT FROM AUTHOR]

Long-term groundwater level analysis, which is usually based on traditionally defined hydrological years is essential in an era of global warming and other climatic and environmental changes, especially in urban areas. A complex interplay of multiple factors influencing the groundwater level makes the investigation of their interdependencies a challenge. Based on multiple data sets and a long time series available as well as specific geological and hydrological conditions, a semi-urban district of Wrocław/Poland was selected as a case study for investigating these dependencies. This paper presents an interdisciplinary approach to the analysis of groundwater level fluctuations by combining mathematics, signal processing, hydrogeology, and meteorology. Applying well-known methods from disciplines other than hydrogeology, the authors investigated seasonal behavior and similarity of groundwater level fluctuations during 15 hydrological years. Based on segmentation and agglomerative clustering (AHP), five classes of groundwater levels fluctuations for predefined hydrologic years and the corresponding seasons were identified and compared to the classification scheme by Pleczyński. Additionally, the relationship between precipitation and groundwater level was investigated using Pearson, Kendall and Spearman correlations. This led to the identification of "typical" and "untypical" seasons for the correlation between the cumulative precipitation sum and groundwater levels. The results presented here will be used for further investigations of groundwater level fluctuations using additional factors and statistical methods. These aim to identify periods that describe similarities better than the commonly used hydrological year. [ABSTRACT FROM AUTHOR]

Numerous circulation indices have been applied in practical climate services focused on regional precipitation. It is beneficial to identify the most influential or decisive indices, but this is difficult with conventional correlation analyses because of the underlying nonlinear mechanisms for precipitation. This paper demonstrates a set of the most influential indices for July–August precipitation in North China, based on the recursive random forest (RRF) method. These decisive circulation indices include the Polar–Eurasia teleconnection, North African subtropical high ridge position, India–Burma trough, Antarctic Oscillation, Northern Hemisphere polar vortex central latitude, North Atlantic Oscillation, and western Pacific subtropical high northern boundary position. Some of these factors have been recognized as directly influential to the regional precipitation, for example, those of the northwestern Pacific subtropical high; however, some are not easily understood. Decision tree (DT) models using these indices were developed to facilitate composite analyses to explain the RRF results. Taking one of the most interesting DT rules as an example, when the North African subtropical high ridge position is sufficiently far south, an anomalous anticyclone occurs in the upstream and an anomalous cyclone in the downstream of North China. This is unfavorable for northward moisture transport in eastern China and hence causes less precipitation in North China than climatology. The present results are not only helpful for improving diagnostic models of regional precipitation, but also enlightening for exploring how global climate change could impact a region by modulating large-scale circulation patterns. [ABSTRACT FROM AUTHOR]

Abstract Climate change, pollution, and deforestation have a negative impact on global mental health. There is an environmental justice dimension to this challenge as wealthy people and high‐income countries are major contributors to climate change and pollution, while poor people and low‐income countries are heavily affected by the consequences. Using state‐of‐the art data mining, we analyzed and visualized the global research landscape on mental health, climate change, pollution and deforestation over a 15‐year period. Metadata of papers were exported from PubMed®, and both relevance and relatedness of terms in different time frames were computed using VOSviewer. Co‐occurrence graphs were used to visualize results. The development of exemplary terms over time was plotted separately. The number of research papers on mental health and environmental challenges is growing in a linear fashion. Major topics are climate change, chemical pollution, including psychiatric medication in wastewater, and neurobiological effects. Research on specific psychiatric syndromes and diseases, particularly on their ethical and social aspects is less prominent. There is a growing body of research literature on links between mental health, climate change, pollution, and deforestation. This research provides a graphic overview to mental healthcare professionals and political stakeholders. Social and ethical aspects of the climate change‐mental health link have been neglected, and more research is needed.

For over three decades, the European Union's (EU) transport policy has aimed at fostering environmental sustainability and energy efficiency. Since 2015, European policymakers have focused more on three key sustainable development goals: decarbonizing the transport system, promoting low-emission mobility solutions, and transitioning to renewable and alternative fuels. To effectively communicate priorities and engage stakeholders, EU policymakers regularly use social media platforms like Twitter (now known as X). This active discourse involves policymakers, industrial stakeholders, the media, and the public, offering insights into the role of transport policy in addressing climate change and energy transition challenges. The current research endeavors to track and analyze the evolution of user-generated content related to climate change, energy transition, and smart mobility on Twitter from 2011 to 2021. This research uses text-mining and social network analysis techniques to quantitatively and qualitatively assess the dynamics of relevant EU policies and their effects. The study's findings can be used to establish a robust monitoring and evaluation framework at the EU and national levels. This framework will assess the effectiveness of communicating strategic priorities for sustainable transport development. It also holds potential for application in other sectors, broadening its impact. [ABSTRACT FROM AUTHOR]

Abstract Big climate change data have become a pressing issue that organizations face with methods to analyze data generated from various data types. Moreover, storage, processing, and analysis of data generated from climate change activities are becoming very massive, and are challenging for the current algorithms to handle. Therefore, big data analytics methods are designed for significantly large amounts of data required to enhance seasonal change monitoring and understand and ascertain the health risks of climate change. In addition, analysis of climate change data would improve the allocation, and utilisation of natural resources. This paper provides an extensive discussion of big data analytic methods for climate data analysis and investigates how climate change and sustainability issues can be analyzed through these approaches. We further present the big data analytic methods, strengths, and weaknesses, and the essence of analyzing big climate change using these methods. The common datasets, implementation frameworks for climate change modeling, and future research directions were also presented to enhance the clarity of these compelling climate change analysis challenges. This big data analytics method is well-timed to solve the inherent issues of data analysis and easy realization of sustainable development goals.

Several biological research studies have shown that the number of individuals of certain species of anurans in a specific geographical region, and the evolution of this number over time, can be used as an indicator of climate change. To detect the presence of anurans, Wireless Sensor Networks (WSNs) are usually deployed with the aim of obtaining bio-acoustic information in a set covering numerous locations. However, the identification of the anuran species from a huge number of recordings usually involves an overwhelming task that has to be undertaken by expert and intelligent systems. Previous studies into this issue have proposed several classification techniques with a common approach: they all take into account the sequential characteristic of sounds by considering syllables or other kinds of vocal segments. In noisy sounds, as it is usually the case in recordings made in natural habitats, segmentation of the signal is no straightforward task and may cause low classification accuracy. To override this problem, a new non-sequential approach is proposed in this paper. It is based on considering very small pieces of sounds (frames) each of which is then classified without considering preceding or subsequent information. Up to nine frame-based classifiers are explored in this paper and their performances are compared to the most commonly used sequential classifier: the Hidden Markov Model (HMM). Additionally, for featuring the frames, many choices have been described, although the application of the Mel Frequency Cepstral Coefficients (MFCCs) has probably become the most common method. In this work, an alternative methodology is suggested: the use of a set of MPEG-7 parameters, which offers a normalized solution with a much greater semantic content. The experimental results have shown that the proposed method clearly outperforms the HMM, thereby showing the non-sequential classification of anuran sounds to be feasible. From among the algorithms tested, the decision-tree classifier has shown the best performance with an overall classification success rate of 87.30%, which is an especially striking result considering that the analyzed sounds were affected by a decidedly noisy background. [ABSTRACT FROM AUTHOR]

Using the newly available decade-long Argo data for the period 2004-13, a detailed study is carried out on deriving four-dimensional (4D) modality of sea temperature in the upper ocean with emphasis on its interannual variability in terms of amplitude, phase, and periodicity. Three principal modes with central periodicities at 19.2, 33.8, and 50.3 months have been identified, and their relationship with El Niño-Southern Oscillation (ENSO) is investigated, yielding a number of useful results and conclusions: 1) A striking tick-shaped pipe-like feature of interannual variability maxima, which is named the 'Niño pipe' in this paper, has been revealed within the 10°S-10°N upper Pacific Ocean. 2) The pipe core extends downward from ~50 m at 130°E to ~250 m near the date line before tilting upward to the sea surface at about 275°E, coinciding nicely with the pathway of the Pacific equatorial undercurrent (EUC). 3) The double-peak zonal modality pattern of the Niño pipe in the upper Pacific is echoed in the subsurface Atlantic and Indian Oceans through Walker circulation, while its single-peak meridional modality pattern is mirrored in the subsurface North and South Pacific through Hadley circulation. 4) A coherent three-peak modal structure implies a strong coupling between sea level variability at the surface and sea temperature variability around the thermocline. Accumulating evidence suggests that Rossby/Kelvin wave dynamics in tandem with EUC-based thermocline dynamics are the main mechanisms of the three-mode Niño pipe in ENSO cycles. [ABSTRACT FROM AUTHOR]

Climate change has highlighted the need to promote renewable energies. The offshore wind industry is growing exponentially because of some political strategies supported by various organizations, such as the European Union. The implementation of these strategies is commonly associated with significant investments, public acceptance, or achieving better installations and greater cumulative capacities. To ensure that offshore renewable energy projects could reach their ambitious targets, this study promotes the implementation of political strategies or planning decisions using data mining techniques and analytical tools. Strategic decisions based on real data analysis could help to achieve more suitable and optimal infrastructures. The scour phenomenon jeopardizes the operability of offshore wind farms, making it necessary to study its evolution over the years. In this work, extensive research on the scour phenomenon in offshore wind farms using real data (from the Lynn and Inner Dowsing offshore wind farms located in the UK) was performed, which revealed an evident lack of consideration of this phenomenon for data-driven decision-making processes. As a novelty, this research develops a detailed geospatial analysis of data, studying the possible autocorrelation of scour data measured from each turbine between 2011 and 2015. The conclusions obtained could be used to improve future planning tasks in offshore wind farms. [ABSTRACT FROM AUTHOR]

Analysis of temporal climate data is an active research area. Advanced data mining methods designed especially for these temporal data support the domain expert's pursuit to understand phenomena as the climate change, which is crucial for a sustainable world. Important solutions for mining temporal data are cluster tracing approaches, which are used to mine temporal evolutions of clusters. Generally, clusters represent groups of objects with similar values. In a temporal context like tracing, similar values correspond to similar behavior in one snapshot in time. Each cluster can be interpreted as a behavior type and cluster tracing corresponds to tracking similar behaviors over time. Existing tracing approaches are for datasets satisfying two specific conditions: The clusters appear in all attributes, i.e., fullspace clusters, and the data objects have unique identifiers. These identifiers are used for tracking clusters by measuring the number of objects two clusters have in common, i.e. clusters are traced based on similar object sets. These conditions, however, are strict: First, in complex data, clusters are often hidden in individual subsets of the dimensions. Second, mapping clusters based on similar objects sets does not reflect the idea of tracing similar behavior types over time, because similar behavior can even be represented by clusters having no objects in common. A tracing method based on similar object values is needed. In this paper, we introduce a novel approach that traces subspace clusters based on object value similarity. Neither subspace tracing nor tracing by object value similarity has been done before. [ABSTRACT FROM AUTHOR]

Sustainable development as a concept that aims to enhance the quality of life without affecting environment has been in focus in European policies since the last decade of 20th century. The objectives of EU Sustainable Development Strategy (EU SDS) are grouped into ten thematic areas, where almost each theme has at least one headline and several operational sustainable development indicators (SDIs). A large number of SDIs is needed to evaluate all EU SDS goals, which imposes the use of multivariate data mining techniques. This paper presents an empirical study carried out to assess the sustainability performance of European countries using the differential multi criteria analysis (DMCA) technique. The PROMETHEE (Preference Ranking Organization MeTHod for Enrichment Evaluations) was applied on 38 headline and operational SDIs defined under the EU SDS. This DMCA was applied to 30 European countries over a 10-year period (2004–2014) with the aim to determine the theme specific, as well as, overall sustainability progress. The DMCA reveals that the majority of European countries have made progress in sustainability in the studied period, where Czech Republic, Germany, Hungary and Sweden, have enhanced their sustainability performance concerning all themes. There are only two countries, namely Greece and Ireland, which have not made overall progress in this period. Also, above the average progress has been made concerning social inclusion, sustainable transport, and climate change and energy, while in all other themes additional efforts should be made in order to ensure sustainable performance progress in future years. Although the progress in reducing uneven development between EU members has been made, after 2009 a positive trend can be observed only in a limited number of SDS themes. [ABSTRACT FROM AUTHOR]

This paper describes the development of a Climate Change Toolkit (CCT) to perform tasks needed in a climate change study plus projection of extreme weather conditions by analyzing historical weather patterns. CCT consists of Data Extraction, Global Climate Data Management, Bias Correction and Statistical Downscaling, Spatial Interpolation, and Critical Consecutive Day Analyzer (CCDA). CCDA uses a customized data mining approach to recognize spatial and temporal patterns of extreme events. CCT is linked to an archive of 0.5° historical global daily dataset (CRU, 1970–2005), and GCM data (1960–2099) for five models and four carbon scenarios. Application of CCT in California using ensemble results of scenario RCP8.5 showed a probable increase in the frequency of dry periods in the southern part of the region, while decreasing in the north. The frequency of wet periods may suggest higher risks of flooding in the north and coastal strips. We further found that every county in northern California may experience flooding conditions of 1986 at least once between 2020 and 2050. [ABSTRACT FROM AUTHOR]

Addressing the issue of shrinking saline lakes around the globe has turned into one of the most pressing issues for sustainable water resource management. While it has been established that natural climate variability, human interference, climate change, or a combination of these factors can lead to the depletion of saline lakes, it is crucial to investigate each case and diagnose the potential causes of this devastating phenomenon. On that note, this study aims to promote a comprehensive analytical framework that can reveal any significant depletion patterns in lakes while analyzing the potential reasons behind these observed changes. The methodology used in this study is based on statistical analysis, data mining techniques, and remote sensing-based datasets. To achieve the objective of this study, Maharlou Lake has been selected to demonstrate the application of the proposed framework. The results revealed two types of depletion patterns in the lake's surface area: a sharp breaking point in 2007/2008 and a gradual negative trend, which was more pronounced in dry seasons and less prominent in wet seasons. Furthermore, the analysis of hydro-climatic variables has indicated the presence of abrupt and gradual changes in these variables' time series, which could be interpreted as a signal that climate change and anthropogenic drought are changing the basin's status quo. Lastly, analyzing the statistically significant correlation between hydro-climatic variables and the lake's surface area showed the potential connection between the observed changing patterns. The results obtained from data mining models suggest that Maharlou Lake has undergone a morphological transformation and is currently adopting these new conditions. If preventive measures are not taken to revive Maharlou Lake, the tipping point might have been reached, and reviving the lake could be improbable, if not impossible. [ABSTRACT FROM AUTHOR]

In the context of the increased climate change, local weather monitoring is crucial from economic and agricultural point of view, as the classical meteo prediction schemes do not apply very precisely. Therefore, the current paper presents a data mining system capable of gathering weather data from several weather stations and predicting the moisture of the soil for the next day, all in real time. The system has been tested in real-life conditions, with ten stations working in Transylvanian Plain. We have proven that the prediction accuracy is very high and the system can be used in agriculture as a mature platform in any geo-climate conditions. [ABSTRACT FROM AUTHOR]

The paper investigates how climate changes and socioeconomic transformations affected grassland productivity from 2000 to 2017 in counties in Inner Mongolia of China. The grassland productivity and climatic change variables included 162 readings (9 periods in the growing season × 18 years), while ten socioeconomic variables contained 18 yearly readings. The mismatch between the environmental observations and socioeconomic data compounds quantitative analysis of causal relations between them. We applied several time-series processing methods to generate five datasets. We fit them with conventional, dependent-lagged, and dynamic-lagged panel regression models to investigate which combinations augment causal relationships between grassland productivity, climate, and socioeconomic changes. The results confirmed that the conventional model with the original data failed to reveal causal relationships between these factors. The panel regression models with the original data tended to intensify the causal effects of climate factors on grassland productivity. The models with the transformed datasets disclosed more socioeconomic variables showing causal relationships with grassland productivity. The dependent-lagged model with the empirical mode decomposition transformed data produced the highest R-squared value and lowest prediction error. GDP, state fixed asset investment, highway construction, grain plantation, and livestock density were primary factors affecting grassland productivity. In brief, the integration of time-series data mining and time-lagged panel analysis effectively unveiled the causes of grassland deterioration under climate and socioeconomic changes. [Display omitted] • Augment causality hidden in env-socio-economic systems with data assimilation. • Assimilate temporal environmental and socioeconomic data into compatible time-series. • Experiment with four groups of computational data mining methods on data assimilation. • Compare combinations of data assimilation methods and lagged panel regression models. [ABSTRACT FROM AUTHOR]

Climate change is a serious threat to humankind. As broad public participation is required in climate change mitigation efforts, it is critical to understand how the public talk about climate change on social media. This study sets out to increase the understanding of Chinese public awareness of climate change, as well as explore the potential and limitations of social media for public engagement on climate change issues. It examines the Chinese public's discussion about climate change on social media Weibo during the last six years through data mining and text analysis. The analyses include volume analysis, keyword extraction, topic modeling, and sentiment analysis. The results indicate three main aspects of public awareness and concern regarding climate change. First, public awareness of climate change is growing in China. Second, the sentiment analysis shows that the general sentiment toward climate change is becoming more positive over time. Third, based on keyword extraction and topic modeling, the discussion on climate change shows a top-down perspective, an optimistic economic perspective, and a preference for celebrity content. The study provides a comprehensive picture of Chinese social media users' views on climate change issues, based on large-scale research data. It contributes to a better understanding of what Chinese people think about climate change on social media generally. These findings may provide government and environmental organizations with valuable insights for better climate change campaigns on social media. [ABSTRACT FROM AUTHOR]

Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic). In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs. The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS, t-statistic (t), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods. This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals. [ABSTRACT FROM AUTHOR]

Bridge owners are faced with the daunting task of maintaining or replacing aging infrastructure over the next century. Added to this challenge are climate change projections such as rising sea levels. A major concern to bridge owners is the need to strengthen the resiliency of their bridges while utilizing a limited amount of financial resources. This paper will offer a methodology for prioritizing the vulnerability to flooding and scour for a state department of transportation's bridge inventory. Through the use of geographic information system (GIS) software, data is mined from the National Bridge Inventory (NBI) - making this methodology applicable to any state agency in the country. The New York City metropolitan region will be presented as a case study. [ABSTRACT FROM AUTHOR]

One of the most crucial issues in water and food security is to assess the impacts of climate change on virtual water content (VWC) and crop yield of agricultural products. The objective of this study is to efficiently predict the VWC patterns and yields of different crops under various climate change conditions using three data mining approaches including artificial neural network (ANN), genetic programming (GP), and support vector machine (SVM). The study region included the eastern provinces of Iran containing North Khorasan, Khorasan Razavi, South Khorasan, Sistan-Baluchestan (in a range of latitude from 25 to 40°N). Specifically, VWC and crop yields were estimated for both baseline period (1985–2005) and several climate change conditions including four time horizons (2030, 2050, 2070, and 2090) under RCPs 2.6, 4.5, and 8.5 based on the second generation Canadian Earth System Model (CanESM2). The data mining models were evaluated with the RMSE and NSE goodness-of-fit criteria. The results showed that the SVM model achieved the highest NSE and lowest RMSE values. It was also found that under the climate change conditions, VWC increased from 6 to 42%, while crop yield decreased from 8 to 53% for all products in the southern regions. An opposite trend was observed in the northern regions for wheat and barley with an increase from 12 to 72% for VWC and from 4 to 27% for the yield. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau, known as the world's "Third Pole" due to its high altitude, is experiencing rapid, intense climate change, similar to and even far more than that occurring in the Arctic and Antarctic. Scientific data sharing is very important to address the challenges of better understanding the unprecedented changes in the Third Pole and their impacts on the global environment and humans. The National Tibetan Plateau Data Center (TPDC, http://data.tpdc.ac.cn) is one of the first 20 national data centers endorsed by the Ministry of Science and Technology of China in 2019 and features the most complete scientific data for the Tibetan Plateau and surrounding regions, hosting more than 3,500 datasets in diverse disciplines. Fifty datasets featuring high-mountain observations, land surface parameters, near-surface atmospheric forcing, cryospheric variables, and high-profile article-associated data over the Tibetan Plateau, frequently being used to quantify the hydrological cycle and water security, early warning assessments of glacier avalanche disasters, and other geoscience studies on the Tibetan Plateau, are highlighted in this manuscript. The TPDC provides a cloud-based platform with integrated online data acquisition, quality control, analysis, and visualization capability to maximize the efficiency of data sharing. The TPDC shifts from the traditional centralized architecture to a decentralized deployment to effectively connect Third Pole–related data from other domestic and international data sources. As an embryo of data sharing and management over extreme environment in the upcoming "big data" era, the TPDC is dedicated to filling the gaps in data collection, discovery, and consumption in the Third Pole, facilitating scientific activities, particularly those featuring extensive interdisciplinary data use. [ABSTRACT FROM AUTHOR]

Abstract: Climate-G is a large scale distributed testbed devoted to climate change research. It is an unfunded effort started in 2008 and involving a wide community both in Europe and US. The testbed is an interdisciplinary effort involving partners from several institutions and joining expertise in the field of climate change and computational science. Its main goal is to allow scientists carrying out geographical and cross-institutional data discovery, access, analysis, visualization and sharing of climate data. It represents an attempt to address, in a real environment, challenging data and metadata management issues. This paper presents a complete overview about the Climate-G testbed highlighting the most important results that have been achieved since the beginning of this project. [Copyright &y& Elsevier]

Growing consumption has brought a series of environmental problems. Sustainable consumption patterns which could meet human needs, improve the quality of lives, and reduce pollutants in the product life cycle emerge and develop. With the development and application of information and network technology, the scale and variety of data are increasing rapidly; advances in data analytics have made the economy, and consumption, quantifiable and visible. At present, many scholars rely on a big-data background and carry out research on sustainable consumption. Therefore, we called for sustainable and consumption papers for special volume of Journal of Cleaner Production (JCLPRO). We received submissions from all over the world and eventually accepted 45. This Special Issue forming a study on sustainable energy consumption, low-carbon transportation, waste recovery and recycling, climate change cost assessment, application and policy modelling for big data and sustainable consumption to promote sustainable development in the fields of energy consumption, low-carbon transportation, waste recovery, and so on. The authors have analysed the problems of pollution and carbon emission in different regions and product production cycles, according to the background of specific regions and enterprises, through data mining, measurement models, and an evaluation index system. Some suggestions are provided for urban construction and enterprise development according to the results. [ABSTRACT FROM AUTHOR]

Public attention and interest in the fate of endangered species is a crucial prerequisite for effective conservation programs. Societal awareness and values will largely determine whether conservation initiatives receive necessary support and lead to adequate policy change. Using text data mining, we assessed general public attention in France, Germany and the United Kingdom toward climate change and biological invasions in relation to endangered amphibian, reptile, bird and mammal species. Our analysis revealed that public attention patterns differed among species groups and countries but was globally higher for climate change than for biological invasions. Both threats received better recognition in threatened than in non-threatened species, as well as in native species than in species from other countries and regions. We conclude that more efficient communication regarding the threat from biological invasions should be developed, and that conservation practitioners should take advantage of the existing attention toward climate change. [ABSTRACT FROM AUTHOR]

Purpose: Globally, increase in average mean temperature of the earth has been a hindrance to humanhealth development, agricultural productivity including environmental sustainability. The situation is exacerbated by continual/increase in gas flaring, mismanagement of land that releases nitrous oxide (N2O) including ecological-disturbances and imbalance. To reduce the impact of greenhouse gas emission, especially in low-income regions, then using a bio-approach becomes imperative. Research Method: The work adopted a theoretical approach (data mining), x-raying/amplifying the important role trees plays in our environment towards reducing greenhouse gas emission. Findings: Trees has been found/confirmed to be able to capture and lock carbon in the form of CO2 in their biomass, thereby helping in reducing the amount of CO2 content in the atmosphere. Findings of this study revealed that trees act as a clean mechanism that can be used, and has been able to reduce CO2 content out of the atmosphere. Several reviewed research findings indicated a significant (p<0.05) increase in carbon sequestration potential of various tree species. Research Limitations: Field verification for data obtained through data mining was only verified through literature search. Originality/value: The outcome of the study presented a view that trees can statistically act as a clean biological mechanism approach towards environmental, agricultural including human health sustainability in the face of climate change. [ABSTRACT FROM AUTHOR]

Global climate change models predict an increase in the frequency and intensity of extreme droughts, with uncertain ecological impacts across ecosystems. In particular, it is not clear when extreme droughts will elicit extreme ecological responses.For this study, we employed three complementary approaches to explore the relationships between extreme drought and ecosystem responses. First, we used global data mining to evaluate the relationship between extreme gross primary productivity and extreme precipitation from 1980 to 2013. Second, we conducted a meta‐analysis using 132 drought experiments across the globe to assess the response ratios (RRs) of above‐ground net primary productivity (ANPP) to extreme versus non‐extreme drought treatments. Third, we examined community and ecosystem responses in an alpine meadow to a drought gradient experiment, which included five precipitation treatment levels (1/12 P, 1/4 P, 1/2 P, 3/4 P, and P, where P is the growing season precipitation).This study had three key results. In our historical data mining, we found that extreme droughts elicited extreme ecological responses only 15.1% of the time. The meta‐analysis results indicated that there were no significant differences in the RRs of ANPP between the extreme versus non‐extreme drought treatments. The drought gradient experiment results revealed that although the four drought treatments were statistically extreme, only the most extreme drought treatment (1/12 P) significantly reduced ANPP over the 3 years. Meanwhile, species richness and asynchrony were significantly reduced under the 1/12 P treatment, which led to a significant reduction in productivity.Synthesis. These results suggest that extreme ecological responses to extreme drought may be less frequent than previously thought. But when they do occur, extreme ecological responses may be driven by plant community changes such as species asynchrony, species loss or species reordering. Our experimental results highlight the key role of community dynamics in determining the resistance of ecosystem productivity to extreme drought, which should be assessed when predicting ecological responses to climate change. [ABSTRACT FROM AUTHOR]

Seasonal and inter-annual climate variability have become important issues for farmers, and climate change has been shown to increase them. Simultaneously farmers and agricultural organizations are increasingly collecting observational data about in situ crop performance. Agriculture thus needs new tools to cope with changing environmental conditions and to take advantage of these data. Data mining techniques make it possible to extract embedded knowledge associated with farmer experiences from these large observational datasets in order to identify best practices for adapting to climate variability. We introduce new approaches through a case study on irrigated and rainfed rice in Colombia. Preexisting observational datasets of commercial harvest records were combined with in situ daily weather series. Using Conditional Inference Forest and clustering techniques, we assessed the relationships between climatic factors and crop yield variability at the local scale for specific cultivars and growth stages. The analysis showed clear relationships in the various location-cultivar combinations, with climatic factors explaining 6 to 46% of spatiotemporal variability in yield, and with crop responses to weather being non-linear and cultivar-specific. Climatic factors affected cultivars differently during each stage of development. For instance, one cultivar was affected by high nighttime temperatures in the reproductive stage but responded positively to accumulated solar radiation during the ripening stage. Another was affected by high nighttime temperatures during both the vegetative and reproductive stages. Clustering of the weather patterns corresponding to individual cropping events revealed different groups of weather patterns for irrigated and rainfed systems with contrasting yield levels. Best-suited cultivars were identified for some weather patterns, making weather-site-specific recommendations possible. This study illustrates the potential of data mining for adding value to existing observational data in agriculture by allowing embedded knowledge to be quickly leveraged. It generates site-specific information on cultivar response to climatic factors and supports on-farm management decisions for adaptation to climate variability. [ABSTRACT FROM AUTHOR]