Review of comparative studies on market mechanisms for carbon emission reduction: a bibliometric analysis

This study uses the bibliometric method to analyze the comparative studies on different market mechanisms applied to carbon reduction between 1970 and 2016 based on the online databases of Science Citation Index Expanded (1970–2016) and Social Science Citation Index (2002–2016). We found by observing the characteristics of publications that such studies belong to a multidisciplinary field that has been continuously developing since the 1990s. The USA and the UK have maintained their leading research strengths in the field, whereas China entered late but has developed rapidly. The most productive journal, institution, and author in this field are Energy Policy, Resources for the Future, and Pizer from the USA, respectively. The auctorial and institutional cooperation degrees are growing and still have a broad collaboration space, but the cooperation among countries is fluctuating at a lower level. Nonetheless, the USA keeps its significant international cooperation ties. The bibliographic coupling and co-citation analysis reveals that articles in this field are closely related to one another. The basic literature in the field was written by Weitzman and published in 1974, and the article by Nordhaus published in Science in 1992 is the most cited in the field. The analysis of keywords and abstract shows that the hot spots include policy choice, price-versus-quantity analysis, and mechanism design.     

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

In this paper, we evaluate the predictive performance of an adaptive neuro-fuzzy inference system (ANFIS) using six different membership functions (MF). In combination with a geographic information system (GIS), ANFIS was used for land subsidence susceptibility mapping (LSSM) in the Marand plain, northwest Iran. This area is prone to droughts and low groundwater levels and subsequent land subsidence damages. Therefore, a land subsidence inventory database was created from an extensive field survey. Areas of land subsidence or areas showing initial signs of subsidence were used for training, while one-third of inventory database were reserved for testing and validation. The inventory database randomly divided into three different folds of the same size. One of the folds was chosen for testing and validation. Other two folds was used for training. This process repeated for every fold in the inventory dataset. Thereafter, land subsidence related factors, such as hydrological and topographical factors, were prepared as GIS layers. Areas susceptible to land subsidence were then analyzed using the ANFIS approach, and land subsidence susceptibility maps were created, whereby six different MFs were applied. Lastly, the results derived from each MF were validated with those areas of the land subsidence database that were not used for training. Receiver operating characteristics (ROC) curves were drawn for all LSSMs, and the areas under the curves were calculated. The ROC analyses for the six LSSMs yielded very high prediction values for two out of the six methods, namely the difference of DsigMF (0.958) and GaussMF (0.951). The integration of ANFIS and GIS generally led to high LSSM prediction accuracies. This study demonstrated that the choice of training dataset and the MF significantly affects the results.     

Mining exploitation influence range

Mining exploitation has a negative impact on the natural environment. Voids created in the rockmass result in displacements and deformations of land surface. During planning and conducting the exploitation, the range of exploitation influence in the form of linear deformations is being determined. On the basis of mining-geological parameters of exploitation, the exploitation range of influences is calculated. According to the literature, many different ranges of exploitation influences can be determined depending on what has been the purpose of it. Different types of exploitation influence ranges can be distinguished, such as theoretical, damage or measurable. In the paper, the matters connected with determining those three types of the influence range are taken under consideration. The comparison of magnitudes of determined influence ranges is illustrated with two practical examples.     

Quantile regression C-vine copula model for spatial extremes

A spatial quantile regression model is proposed to estimate the quantile curve for a given probability of non-exceedance, as function of locations and covariates. Canonical vines copulas are considered to represent the spatial dependence structure. The marginal at each location is an asymmetric Laplace distribution where the parameters are functions of the covariates. The full conditional quantile distribution is given using the Joe–Clayton copula. Simulations show the flexibility of the proposed model to estimate the quantiles with special dependence structures. A case study illustrates its applicability to estimate quantiles for spatial temperature anomalies.     

Archaeoseismological evidence of past earthquakes in Rome (fifth to ninth century A.D.) used to quantify dating uncertainties and coseismic damage

The transformation of Rome during the Late Antiquity and the Early Middle Ages has been investigated by archaeologists and historians. Social and political changes are the main aspects which led to a progressive modification of the urban framework; abandonment, spoliation and transformation of buildings are quite diffused as documented by the archaeological literature. The consequence of these practices is a higher vulnerability of the buildings which, from the seismological point of view, played a main role in increasing the effects of seismic shaking. A number of earthquakes have struck Rome during the period of investigation (fifth to ninth century A.D.), known from historical sources: 443, 484–508, 618, 801, 847; in some cases (443, 484–508, 801) damage has been documented. In contrast, the archaeological sources characterise collapse layers and evidence of destruction at different sites with changing and not always conclusive chronological constraints. Consequently, collapse and destruction have been alternatively attributed to the above-mentioned earthquakes. Through a geoarchaeological and stratigraphic analysis of potentially coseismic collapse units, we want (1) to describe the archaeoseismic evidence derived from recent excavations and from the available literature (e.g. Piazza Madonna di Loreto, Piazza Venezia, Palazzo Valentini Crypta Balbi, Colosseo, Basilica Hilariana, Basilica di Santa Petronilla, Santa Maria Antiqua,…); (2) to discuss the chronological problems and the uncertainty of attribution of the collapse units to known historical earthquakes; (3) to discuss the earthquake damage exaggeration due to erroneous attribution of seismic origin to the evidence of destruction derived from archaeological data. Finally, we will infer the role that earthquakes may have had on the development of the urban landscape in the fifth to ninth century A.D.     

Socioeconomic vulnerability and electric power restoration timelines in Florida: the case of Hurricane Irma

Large-scale damage to the power infrastructure from hurricanes and high-wind events can have devastating ripple effects on infrastructure, the broader economy, households, communities, and regions. Using Hurricane Irma’s impact on Florida as a case study, we examined: (1) differences in electric power outages and restoration rates between urban and rural counties; (2) the duration of electric power outages in counties exposed to tropical storm force winds versus hurricane Category 1 force winds; and (3) the relationship between the duration of power outage and socioeconomic vulnerability. We used power outage data for the period September 9, 2017–September 29, 2017. At the peak of the power outages following Hurricane Irma, over 36% of all accounts in Florida were without electricity. We found that the rural counties, predominantly served by rural electric cooperatives and municipally owned utilities, experienced longer power outages and much slower and uneven restoration times. Results of three spatial lag models show that large percentages of customers served by rural electric cooperatives and municipally owned utilities were a strong predictor of the duration of extended power outages. There was also a strong positive association across all three models between power outage duration and urban/rural county designation. Finally, there is positive spatial dependence between power outages and several social vulnerability indicators. Three socioeconomic variables found to be statistically significant highlight three different aspects of vulnerability to power outages: minority groups, population with sensory, physical and mental disability, and economic vulnerability expressed as unemployment rate. The findings from our study have broader planning and policy relevance beyond our case study area, and highlight the need for additional research to deepen our understanding of how power restoration after hurricanes contributes to and is impacted by the socioeconomic vulnerabilities of communities.     

An agent-based evacuation model for the 2011 Brisbane City-scale riverine flood

In this study, an agent-based model is proposed in order to provide new insights into the policy analysis and strategy assessment of city-scale evacuation management. The proposed model is suitable for assessment of the influence of different departure times and communications among peer evacuees on the number of residents at risk who arrive at official shelters. A case study is applied to build a simulation model for the coastal city of Brisbane in Australia. The Brisbane River catchment experiences regular flooding almost every year; the second severest flood since the twentieth century occurred in 2011. During that event, over 15,000 properties were inundated and around 3600 households evacuated in metropolitan Brisbane alone. Making use of high-performance computing clusters, the evacuation simulation was coupled with results from a validated hydrodynamic model to test a variety of escaping scenarios based on the 2011 flood situation. This case study demonstrates the proposed model’s capacity to represent the dynamic evacuation process and also shows that the model is able to help develop flood emergency plans and evaluate response measures through exploring key elements in a range of scenarios.     

Effect of climatic oscillations on flood occurrence on Papaloapan River,México,during the 1550–2000 period

This study presents a chronology of historical and measured flood events in the Papaloapan River basin of Mexico during 450 years. Twenty-eight historical floods were recorded during the period 1550–1948 on this river and one flood event (1969) in the instrumental era (1949–2000), of which 14 were extraordinary floods and only 15 were catastrophic ones. There were several flood-rich decades during 1860–1870, 1880–1890, 1920–1930 and 1940–1950. Wavelet analysis found a significant flooding periodicity of 58 years. The wavelet coherence analysis found that flooding had an in-phase relationship with the Atlantic Multidecadal Oscillation and also with the Pacific Decadal Oscillation. Logistic regression corroborated that there exists a positive relationship between floods events and these two natural climatic oscillations. The logistic regression model predicted correctly 92% of flood events.     

Application on Floor Water Inrush Evaluation Based on AHP Variation Coefficient Method with GIS

The prediction and prevention of floor water inrush is directly related to the safety of the coal mine production. The previous evaluation method of floor water inrush was more one-sided and lacked main control factors related to mining conditions. In order to evaluate the floor water inrush more accurately, under the project background of geological data of Wanglou coal mine, stope width, mining depth, fault scale index, water pressure, water abundance and thickness of aquifer were selected as main controlling factors of floor water inrush. Combined with the subjective weight analytical hierarchy process and the objective weight variation coefficient method, the weight coefficients corresponding to the main controlling factors were obtained respectively. The thematic map of the risk assessment of coal seam floor water inrush was drawn by combining the constructed comprehensive weight vulnerability index model and geographic information system. The results show that: ① according to the actual geological data of mine, two fault related factors were removed. And stope width and mining depth were increased as the main controlling factors to evaluate floor water inrush. It is easier to compare and calculate the weight of evaluation factors. ② The constructed comprehensive weight vulnerability index model can comprehensively evaluate the risk of floor water inrush. And the results of the evaluation are more accurate. ③ The related thematic maps can directly reflect the risk of floor water inrush, which is of guiding significance for the prediction and prevention of coal seam floor water inrush.     

Numerical ANFIS-Based Formulation for Prediction of the Ultimate Axial Load Bearing Capacity of Piles Through CPT Data

This study explores the potential of adaptive neuro-fuzzy inference systems (ANFIS) for prediction of the ultimate axial load bearing capacity of piles (P_u) using cone penetration test (CPT) data. In this regard, a reliable previously published database composed of 108 datasets was selected to develop ANFIS models. The collected database contains information regarding pile geometry, material, installation, full-scale static pile load test and CPT results for each sample. Reviewing the literature, several common and uncommon variables have been considered for direct or indirect estimation of P_u based on static pile load test, cone penetration test data or other in situ or laboratory testing methods. In present study, the pile shaft and tip area, the average cone tip resistance along the embedded length of the pile, the average cone tip resistance over influence zone and the average sleeve friction along the embedded length of the pile which are obtained from CPT data are considered as independent input variables where the output variable is P_u for the ANFIS model development. Besides, a notable criticism about ANFIS as a prediction tool is that it does not provide practical prediction equations. To tackle this issue, the obtained optimal ANFIS model is represented as a tractable equation which can be used via spread sheet software or hand calculations to provide precise predictions of P_u with the calculated correlation coefficient of 0.96 between predicted and experimental values for all of the data in this study. Considering several criteria, it is represented that the proposed model is able to estimate the output with a high degree of accuracy as compared to those results obtained by some direct CPT-based methods in the literature. Furthermore, in order to assess the capability of the proposed model from geotechnical engineering viewpoints, sensitivity and parametric analyses are done.