Subjects -> STATISTICS (Total: 130 journals)
 Showing 1 - 151 of 151 Journals sorted alphabetically Advances in Complex Systems       (Followers: 10) Advances in Data Analysis and Classification       (Followers: 52) Applied Categorical Structures       (Followers: 4) Argumentation et analyse du discours       (Followers: 7) Asian Journal of Mathematics & Statistics       (Followers: 8) AStA Advances in Statistical Analysis       (Followers: 2) Australian & New Zealand Journal of Statistics       (Followers: 12) Biometrical Journal       (Followers: 9) Biometrics       (Followers: 51) British Journal of Mathematical and Statistical Psychology       (Followers: 17) Building Simulation       (Followers: 2) CHANCE       (Followers: 5) Communications in Statistics - Simulation and Computation       (Followers: 9) Communications in Statistics - Theory and Methods       (Followers: 11) Computational Statistics       (Followers: 15) Computational Statistics & Data Analysis       (Followers: 35) Current Research in Biostatistics       (Followers: 8) Decisions in Economics and Finance       (Followers: 12) Demographic Research       (Followers: 14) Engineering With Computers       (Followers: 5) Environmental and Ecological Statistics       (Followers: 7) ESAIM: Probability and Statistics       (Followers: 4) Extremes       (Followers: 2) Fuzzy Optimization and Decision Making       (Followers: 8) Geneva Papers on Risk and Insurance - Issues and Practice       (Followers: 11) Handbook of Numerical Analysis       (Followers: 5) Handbook of Statistics       (Followers: 7) IEA World Energy Statistics and Balances -       (Followers: 2) International Journal of Computational Economics and Econometrics       (Followers: 6) International Journal of Quality, Statistics, and Reliability       (Followers: 17) International Journal of Stochastic Analysis       (Followers: 2) International Statistical Review       (Followers: 12) Journal of Algebraic Combinatorics       (Followers: 3) Journal of Applied Statistics       (Followers: 20) Journal of Biopharmaceutical Statistics       (Followers: 23) Journal of Business & Economic Statistics       (Followers: 38, SJR: 3.664, CiteScore: 2) Journal of Combinatorial Optimization       (Followers: 7) Journal of Computational & Graphical Statistics       (Followers: 21) Journal of Econometrics       (Followers: 82) Journal of Educational and Behavioral Statistics       (Followers: 7) Journal of Forecasting       (Followers: 19) Journal of Global Optimization       (Followers: 6) Journal of Mathematics and Statistics       (Followers: 6) Journal of Nonparametric Statistics       (Followers: 6) Journal of Probability and Statistics       (Followers: 10) Journal of Risk and Uncertainty       (Followers: 34) Journal of Statistical and Econometric Methods       (Followers: 3) Journal of Statistical Physics       (Followers: 13) Journal of Statistical Planning and Inference       (Followers: 7) Journal of Statistical Software       (Followers: 16, SJR: 13.802, CiteScore: 16) Journal of the American Statistical Association       (Followers: 72, SJR: 3.746, CiteScore: 2) Journal of the Korean Statistical Society Journal of the Royal Statistical Society Series C (Applied Statistics)       (Followers: 36) Journal of the Royal Statistical Society, Series A (Statistics in Society)       (Followers: 28) Journal of the Royal Statistical Society, Series B (Statistical Methodology)       (Followers: 41) Journal of Theoretical Probability       (Followers: 3) Journal of Time Series Analysis       (Followers: 16) Journal of Urbanism: International Research on Placemaking and Urban Sustainability       (Followers: 23) Law, Probability and Risk       (Followers: 6) Lifetime Data Analysis       (Followers: 7) Mathematical Methods of Statistics       (Followers: 4) Measurement Interdisciplinary Research and Perspectives       (Followers: 1) Metrika       (Followers: 4) Monthly Statistics of International Trade - Statistiques mensuelles du commerce international       (Followers: 3) Multivariate Behavioral Research       (Followers: 8) Optimization Letters       (Followers: 2) Optimization Methods and Software       (Followers: 6) Oxford Bulletin of Economics and Statistics       (Followers: 33) Pharmaceutical Statistics       (Followers: 16) Queueing Systems       (Followers: 7) Research Synthesis Methods       (Followers: 7) Review of Economics and Statistics       (Followers: 138) Review of Socionetwork Strategies Risk Management       (Followers: 17) Sankhya A       (Followers: 3) Scandinavian Journal of Statistics       (Followers: 9) Sequential Analysis: Design Methods and Applications Significance       (Followers: 7) Sociological Methods & Research       (Followers: 40) SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques Stata Journal       (Followers: 8) Statistica Neerlandica       (Followers: 1) Statistical Inference for Stochastic Processes       (Followers: 3) Statistical Methods and Applications       (Followers: 6) Statistical Methods in Medical Research       (Followers: 27) Statistical Modelling       (Followers: 18) Statistical Papers       (Followers: 4) Statistics & Probability Letters       (Followers: 13) Statistics and Computing       (Followers: 13) Statistics and Economics Statistics in Medicine       (Followers: 122) Statistics: A Journal of Theoretical and Applied Statistics       (Followers: 12) Stochastic Models       (Followers: 2) Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports       (Followers: 2) Structural and Multidisciplinary Optimization       (Followers: 11) Teaching Statistics       (Followers: 8) Technology Innovations in Statistics Education (TISE)       (Followers: 2) TEST       (Followers: 2) The American Statistician       (Followers: 25) The Canadian Journal of Statistics / La Revue Canadienne de Statistique       (Followers: 10) Wiley Interdisciplinary Reviews - Computational Statistics       (Followers: 1)
Similar Journals
 Environmental and Ecological StatisticsJournal Prestige (SJR): 0.594 Citation Impact (citeScore): 1Number of Followers: 7      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1573-3009 - ISSN (Online) 1352-8505 Published by Springer-Verlag  [2469 journals]
• Spatio-temporal analysis of air pollution in North China Plain

Abstract: Accompanying China’s rapid industrialization, a vast area of the country, particularly the Beijing–Tianjin–Hebei (BTH) region, has significantly experienced concerning levels of air pollution over the past decade. Exposure to severe particulate matter (PM), $$PM_{2.5}$$ in particular, it raises a crucial public health concern, but quantifying $$PM_{2.5}$$ accurately across large geographic areas and across time poses a great challenge. To investigate $$PM_{2.5}$$ concentration in the BTH region, we utilize a spatio-temporal mixed effects model that includes geographic information system-based time-invariant spatial variables and time-varying meteorological covariates. Our kriging results find that $$PM_{2.5}$$ concentration is hazardous in the North China Plain (NCP), where major iron, steel, and cement industries are located. More importantly, our analysis of the impact of wind finds that the severity of air pollution highly depends on the direction of the wind. That is, a northerly wind can considerably reduce the level of $$PM_{2.5}$$ in the NCP, while a southerly wind generally does not alleviate air pollution and sometimes even increases it. Using prediction error as a proxy for the level of local emissions, we find that Shijiazhuang and Tangshan produce the most significant local emissions, which coincides with a heavier industry in these two cities. During the winter heating period, we find that the two densely populated cities of Beijing and Tianjin have dramatic increases in local emissions because of the massive coal consumption during this period.
PubDate: 2022-06-01

• Tests for aggregated dispersion: Van Valen’s test and a new
competitor

Abstract: Van Valen’s test is usually applied as a two sample test for equality of dispersion for multivariate data. Motivated by a comment of Manly (Van Valen’s test. Encyclopedia of Statistical Sciences, 2006) that “Little is known about the properties of Van Valen’s test” we develop an alternative test and compare the Van Valen test with our alternative robust test in an extensive simulation study. We find that Van Valen’s test does not actually test for equality of variance sums; however, for that null hypothesis it still performs well in terms of closeness to the nominal significance level. Due to testing the correct null hypothesis and the excellent adherence to the nominal significance level, we recommend the use of the robust test as a permutation test.
PubDate: 2022-06-01

• Is globalisation linked to CO2 emission' Evidence from OECD nations

Abstract: An extensive number of studies uses trade-to-GDP as a proxy for globalisation in environmental research. Globalisation encompasses much more than just trade in goods. Globalisation is the integration of various countries and includes spillovers of ideas and technology, financial flows, the worldwide movement of labour, and national governments meeting on an international level in a bid to solve social and political problems. This study considers the effect of globalisation on carbon dioxide emissions by using a more flexible and comprehensive measure based on the KOF globalisation index for a panel of 21 OECD nations covering the period 1970–2014. Since the globalisation process is not uniform across countries and time, we use a fully-fledged nonparametric technique to estimate the time-varying coefficient and trend functions. Our results show that the effect of globalization on CO2 emissions is positive up until 2000, then switches to turns negative thereafter.
PubDate: 2022-06-01

• New methods of life expectancy estimation

Abstract: Two novel methods of life expectancy estimation, applied to various annual reported demographic datasets, are proposed. First, for datasets that fully recorded birth date and death date of all dead individuals, we rely on the well-known Kaplan–Meier estimation method to provide an accurate estimation framework of life expectancy. Our proposed method can be used as a gold standard in the accuracy investigation of other life expectancy estimation methods. The method can be applied for small areas, where complete mortality data are regularly produced by routine annual surveys. The second new created method, called as local parametric method, based on the theoretical background of survival process with local parametric Weibull distributions, estimates life expectancy using abridged survival data. Experiments on real longitudinal datasets show the new method provides very exact life expectancy estimations for 10 among 15 one-year datasets, whilst the method of Chiang often yields overestimations.
PubDate: 2022-05-13

• Modeling Dinophysis in Western Andalucía using a autoregressive
hidden Markov model

Abstract: Dinophysis spp. can produce diarrhetic shellfish toxins (DST) including okadaic acid and dinophysistoxins, and some strains can also produce non-diarrheic pectenotoxins. Although DSTs are of human health concern and have motivated environmental monitoring programs in many locations, these monitoring programs often have temporal data gaps (e.g., days without measurements). This paper presents a model for the historical time-series, on a daily basis, of DST-producing toxigenic Dinophysis in 8 monitored locations in western Andalucía over 2015–2020, incorporating measurements of algae counts and DST levels. We fitted a bivariate hidden Markov Model (HMM) incorporating an autoregressive correlation among the observed DST measurements to account for environmental persistence of DST. We then reconstruct the maximum-likelihood profile of algae presence in the water column at daily intervals using the Viterbi algorithm. Using historical monitoring data from Andalucía, the model estimated that potentially toxigenic Dinophysis algae is present at greater than or equal to 250 cells/L between< 1% and>10% of the year depending on the site and year. The historical time-series reconstruction enabled by this method may facilitate future investigations into temporal dynamics of toxigenic Dinophysis blooms.
PubDate: 2022-05-04

• A varying-coefficient regression approach to modeling the effects of wind
speed on the dispersion of pollutants

Abstract: Abstract The real-world monitoring system of air pollution ordinarily collects data about pollutant concentration levels at pollution sources and monitors stations in a high-frequency manner. Inspired atmospheric models, the meteorological conditions could play an important role in building up the data-driven model for dispersing atmospheric pollutants from pollution sources to monitor stations. We propose a varying-coefficient model to analyze how emissions of monitor stations are influenced by pollution sources with changing with the wind speed. To estimate the unknown coefficient curves, we use a spline basis to approximate the functions. The asymptotic properties of the proposed method are studied and show the consistency of the estimator. Inference procedures based on a resampling subject bootstrap is developed to construct the conservative confidence bands. A simulation study is carried out to demonstrate the performance of our method. Illustrated by a real-world dataset of environmental sensors collected in Shenyang, China, the proposed varying-coefficient model reveals that the wind speed changes the dispersion mechanism of atmospheric pollutants between monitor stations and pollution sources.
PubDate: 2022-04-23

• Estimating change in annual timber products output using a stratified
sampling with certainty design

Abstract: Abstract A key aspect in understanding patterns in wood demand and harvesting activities is monitoring of timber products output by wood processing facilities. Estimation of change from year-to-year is necessary but is complicated due to shifts in the population as well as changing strata over time. Taking independent samples each year eases complexity, yet suffers from relatively large sampling error in comparison to other designs that take advantage of the covariance arising from correlated samples. In this study, a design intended to maximize the precision of the change estimate by retaining the initial sample to the extent possible was analyzed. Several approaches to estimating the covariance, with the primary challenge being that sometimes only a single sample unit occurred in both samples within a given stratum. Variance underestimation and overestimation were encountered depending on the covariance method. The best outcome was attained using a measure-of-size variable at the population level to approximate the covariance. However, this approach overestimated the variance by 11% in a Monte Carlo simulation. The simulation results suggested a 14% reduction in the standard error of the estimate was attainable from correlated samples relative to independent samples. Due to the challenges introduced for estimating the covariance for changing populations and strata over time, the value of relatively small reductions in sampling error need to be considered in the context of introducing complex and potentially unreliable covariance estimation methods.
PubDate: 2022-03-23

• Inference and model determination for temperature-driven non-linear
ecological models

Abstract: Abstract This paper is concerned with a contemporary Bayesian approach to the effect of temperature on developmental rates. We develop statistical methods using recent computational tools to model four commonly used ecological non-linear mathematical curves that describe arthropods’ developmental rates. Such models address the effect of temperature fluctuations on the developmental rate of arthropods. In addition to the widely used Gaussian distributional assumption, we also explore Inverse Gamma-based alternatives, which naturally accommodate adaptive variance fluctuation with temperature. Moreover, to overcome the associated parameter indeterminacy in the case of no development, we suggest the zero-inflated Inverse Gamma model. The ecological models are compared graphically via posterior predictive plots and quantitatively via marginal likelihood estimates and Information criteria. Inference is performed using the Stan software and we investigate the statistical and computational efficiency of its Hamiltonian Monte Carlo and Variational Inference methods. We also explore model uncertainty and employ Bayesian Model Averaging framework for robust estimation of the key ecological parameters.
PubDate: 2022-03-19

• Dynamic impacts of energy use, agricultural land expansion, and
deforestation on CO2 emissions in Malaysia

Abstract: Abstract This study empirically investigates the nexus among energy use, agricultural land expansion, deforestation, and carbon dioxide (CO2) emissions in Malaysia. Time series data from 1990 to 2019 were utilized using the bounds testing (ARDL) approach followed by the Dynamic Ordinary Least Squares (DOLS) method. The DOLS estimate findings show that the energy usage coefficient is positive and significant with CO2 emissions, indicating a 1% increase in energy consumption is related to a 0.91% rise in CO2 emissions. In addition, the coefficient of agricultural land is positive, which indicates that agricultural land expansion by 1% is associated with an increase in CO2 emissions by 0.84% in the long run. Furthermore, the forested area coefficient is negative, which means that decreasing 1% of the wooded area (i.e., deforestation) has a long-term effect of 5.41% increased CO2 emissions. Moreover, the pairwise Granger causality test results show bidirectional causality between deforestation and energy use; and unidirectional causality from energy use to CO2 emissions, agricultural land expansion to CO2 emissions, deforestation to CO2 emissions, agricultural land expansion to energy use, and deforestation to agricultural land expansion in Malaysia. The empirical findings reveal that increased energy use, agricultural land expansion, and deforestation have a negative impact on environmental quality in Malaysia. Thus, the effective implementation of policy measures to promote renewable energy, climate-smart agriculture, and sustainable management of forest ecosystems could be useful for reducing environmental degradation in Malaysia.
PubDate: 2022-03-17

• Hierarchical log Gaussian Cox process for regeneration in uneven-aged
forests

Abstract: Abstract We propose a hierarchical log Gaussian Cox process (LGCP) for point patterns, where a set of points $$\varvec{x}$$ affects another set of points $$\varvec{y}$$ but not vice versa. We use the model to investigate the effect of large trees on the locations of seedlings. In the model, every point in $$\varvec{x}$$ has a parametric influence kernel or signal, which together form an influence field. Conditionally on the parameters, the influence field acts as a spatial covariate in the intensity of the model, and the intensity itself is a non-linear function of the parameters. Points outside the observation window may affect the influence field inside the window. We propose an edge correction to account for this missing data. The parameters of the model are estimated in a Bayesian framework using Markov chain Monte Carlo where a Laplace approximation is used for the Gaussian field of the LGCP model. The proposed model is used to analyze the effect of large trees on the success of regeneration in uneven-aged forest stands in Finland.
PubDate: 2022-03-01

• Using spatial smoothing to model a functional regression estimator to
points on a lattice with application to surface-level ozone in the Eastern
United States

Abstract: Abstract Spatial functional regression methods allow researchers to model spatially dependent functional random variables, often using a kriging-based interpolation method. However, less effort has been devoted towards the goal of modeling the relationship between a scalar response and a functional covariate throughout a spatial domain. Here, we introduce a method to characterize this association by estimating a coefficient function over points on a spatial lattice. Gridded data products are becoming commonplace in many fields, making this approach useful to researchers in many disciplines. As in our data application, we assume that the set of coefficient functions of interest are spatially similar; therefore, we aim to improve our estimates by penalizing dissimilarity between coefficient function estimates at adjacent points on the lattice. The results of our simulation study provide additional support for this approach. We perform an analysis of surface-level ozone in the Eastern US and consider three functional covariates: the vertical temperature profile (VTP), specific humidity, and omega. Our analysis suggests that the VTP and specific humidity may have stronger associations with surface-level O $$_3$$ than omega, and that these relationships differ by region and by altitude. We also propose a permutation-based hypothesis test to determine whether it is reasonable to believe that coefficient functions truly differ from the zero function. Pointwise application of this test suggests that these atmospheric profile variables (APVs) may be useful predictor variables in much of the spatial domain.
PubDate: 2022-03-01

• Halton iterative partitioning master frames

Abstract: Abstract A spatial sampling design determines where sample locations are placed in a study area. To achieve reliable estimates of population characteristics, the spatial pattern of the sample should be similar to the underlying spatial pattern of the population. A reasonable assumption for natural resources is that nearby locations tend to have more similar response values than distant locations. Hence, sample efficiency can be increased by spreading sample locations evenly over a natural resource. A sample that is well-spread over the resource is called spatially balanced and many spatially balanced sampling designs have been proposed in the statistical literature. Robertson et al. (Environ Ecol Stat 25:305–323, 2018) proposed a sampling design that draws spatially balanced samples using a nested partition. This article modifies their partitioning strategy to spatially order a point resource into a highly structured master frame. Samples of consecutive points from the master frame are spatially balanced and these individual samples can be easily incorporated into a broader spatially balanced design for integrated monitoring. Numerical results show that the master frame’s ordering is effective and that a range of samples drawn from it are spatially balanced.
PubDate: 2022-03-01

• Markov regression model for analyzing big data to predict trajectories of
repeated categorical outcomes: an application to $$\hbox {PM}_{2.5}$$ PM
2.5 air pollution data

Abstract: Abstract Fine particulate matter ( $$\text{ PM}_{2.5}$$ ), tiny particles in the air, is air contamination that negatively impacts the environment and human health when levels in the air are high. The elevated level of $$\text{ PM}_{2.5}$$ also reduces visibility and causes the air to appear hazy. Due to its impact on environment and health, almost every country around the world keeps track of $$\text{ PM}_{2.5}$$ air quality level and records the data repeatedly over time in many sites. As the data are collected repeatedly, there is likely to be a natural dependency among the repeated measures of $$\text{ PM}_{2.5}$$ level in a specific site. Modeling and analyzing these repeated data will help policymakers recommend new policies and/or update existing policies. Thus adequate modeling of such data is of enormous interest among the researchers and policymakers. It is noteworthy that as the data are collected repeatedly in immense volume, big data modeling techniques are required for modeling such data. This paper proposed a new modeling framework to analyze and trajectory risk prediction of categorical responses from big data collected repeatedly. We developed a divide and recombine approach to analyzing big data gathered continually. We used the Markov model for data division, and the Markov chain is used to recombine the marginal and conditional probabilities and estimated joint probabilities for trajectory. We illustrated the proposed model using $$\text{ PM}_{2.5}$$ outdoor air pollution data from the United States between the years 2000 to 2020. The performance of the proposed methodology is also checked through bootstrap simulation studies. The proposed methodology will be useful to analyze and trajectory risk prediction of repeatedly measured responses from big data from various fields.
PubDate: 2022-03-01

• Guest Editorial of the SEEM-2019 conference EEST special issue

PubDate: 2022-02-15
DOI: 10.1007/s10651-022-00530-x

• Pivotal discrepancy measures for Bayesian modelling of spatio-temporal
data

Abstract: Abstract Within the field of geostatistics, Gaussian processes are a staple for modelling spatial and spatio-temporal data. Statistical literature is rich with estimation methods for the mean and covariance of such processes (in both frequentist and Bayesian contexts). Considerably less attention has been paid to developing goodness-of-fit tests for assessment of model adequacy. Jun et al. (Environmetrics 25(8):584–595, 2014) introduced a statistical test that uses pivotal discrepancy measures to assess goodness-of-fit in the Bayesian context. We present a modification and generalization of their statistical test. The initial method involves spatial partitioning of the data, followed by evaluation of a pivotal discrepancy measure at each posterior draw to obtain a posterior distribution of pivotal statistics. Order statistics from this distribution are used to obtain approximate p-values. Jun et al. (Environmetrics 25(8):584–595, 2014) use arbitrary partitions based on pre-existing spatial boundaries. The partitions are made to be of equal size. Our contribution is two-fold. We use K-means clustering to create the spatial partitions and we generalise Jun et al.’s approach to incorporate unequal partition sizes. Observations from a spatial or spatio-temporal process are partitioned using an appropriate feature vector that incorporates the geographic location of the observations into subsets (not necessarily of the same size). The method’s viability is illustrated in a simulation study, and in an application to hoki (Macruronus novaezelandiae) catch data from a survey of the sub-Antarctic region.
PubDate: 2022-02-12
DOI: 10.1007/s10651-022-00529-4

• Estimating wild boar density in hunting areas by a probabilistic sampling
of drive counts

Abstract: Abstract The evaluation of wild boar density in a hunting district can be performed by accurate drive counts of boars within the drive areas assigned to each hunting team. Because a complete driving of all the areas is prohibitive, only a subset is driven in a hunting occasion. Results are highly dependent on the subjective choice of these areas. In this study, an objective design-based approach is considered in which areas to be driven are randomly selected one per team in accordance with the one-per-stratum sampling scheme. Because the areas assigned to hunting teams are likely to be close to each other, the one-per-stratum sampling is likely to achieve samples of evenly spread areas. Then, the subsequent step is to choose the selection criterion for the areas and the estimation criterion for exploiting or not the information provided by area sizes. To this purpose, three sampling strategies are considered, together with methods to estimate their precision. These strategies are checked and compared by means of a simulation study performed on artificial populations constructed from the list of drive areas settled in the Province of Massa–Carrara (Italy) and partitioned among 39 hunting teams. Results from artificial populations give clear insights about the most suitable strategy to be used. Drive counts performed in this province in two hunting occasions during 2019 within 39 areas selected by one-per-stratum sampling are adopted as case studies.
PubDate: 2022-01-18
DOI: 10.1007/s10651-021-00527-y

• How should surface elevation table data be analyzed' A comparison of
several commonly used analysis methods and one newly proposed approach

Abstract: Abstract The use of surface elevation table (SET) instruments to monitor elevation changes at low elevation coastal locations has steadily increased in recent years. A primary focus in the analysis of SET data is the estimation of the overall rate of elevation change, and numerous approaches have been used for this purpose. In this work, we compare and contrast several methods used for estimating the true rate of elevation change at SET station locations, including a novel approach proposed in this work that incorporates spatial dependence. We also discuss theoretical properties of one class of estimators, and undertake a comprehensive simulation study. Additionally, we present two case studies where we illustrate these differences using real SET data. All methods considered here tend to produce similar point estimates, but some confidence interval procedures can generate intervals with empirical coverage rates lower than specified. However, the best analysis approach is likely dependent upon selecting the method that best coincides with the true underlying process. Thus, we do not uniformly recommend one approach for all situations. Instead, we suggest carefully weighing potential advantages and disadvantages of each method before conducting analysis, while keeping in mind the ways in which modeling assumptions may impact this decision.
PubDate: 2022-01-09
DOI: 10.1007/s10651-021-00524-1

• Revisiting the carbon emissions hypothesis in the developing and developed
countries: a new panel cointegration approach

Abstract: Abstract Since global warming worsens with economic development and emitted CO2 is one of the main greenhouse gases, it is important to understand the relationship between CO2 emissions and economic growth. The paper applies a new panel cointegration test with cross-sectional dependence and structural breaks to examine this relationship in developed and developing countries, respectively. The results indicate that the “Environmental Kuznets Curve” does not hold in either group. For developing countries, there is neither linear nor quadratic long-term equilibrium relationship between CO2 emissions and economic growth. For developed countries, the quadratic relationship does exist between CO2 emissions and economic growth, whereas the linear one does not. A half of these countries have inverted U-shaped curves, while the other half have U-shaped curves. Besides, most of these countries are still on the rising stage of the curve. This paper gives new insights for policymakers to keep a balance between sustainable economic growth and suitable environmental quality.
PubDate: 2022-01-04
DOI: 10.1007/s10651-021-00526-z

• Modelling multivariate data using product copulas and minimum distance
estimators: an exemplary application to ecological traits

Abstract: Abstract Modelling and applying multivariate distributions is an important topic in ecology. In particular in plant ecology, the multidimensional nature of plant traits comes with challenges such as wide ranges in observations as well as correlations between several characteristics. In other disciplines (e.g., finances and economics), copulas have been proven as a valuable tool for modelling multivariate distributions. However, applications in ecology are still rarely used. Here, we present a copula-based methodology of fitting multivariate distributions to ecological data. We used product copula models to fit multidimensional plant traits, on example of observations from the global trait database TRY. The fitting procedure is split into two parts: fitting the marginal distributions and fitting the copula. We found that product copulas are well suited to model ecological data as they have the advantage of being asymmetric (similar to the observed data). Challenges in the fitting were mainly addressed to limited amount of data. In view of growing global databases, we conclude that copula modelling provides a great potential for ecological modelling.
PubDate: 2022-01-04
DOI: 10.1007/s10651-021-00525-0

• Study on changes of urban spatial pattern and heterogeneity of driving
factors in the Su-Xi-Chang region

Abstract: Abstract Using the Suzhou-Wuxi-Changzhou (referred to as Su-Xi-Chang) region as a case study, this work applied an Exploratory Spatial Data Analysis model to study the characteristics associated with the evolution in the urban spatial patterns in the region from 2002 to 2018. A geographical weighted regression model and Local indicator of spatial association Index are used to analyze the degrees of influence that different driving factors have on urban spatial patterns in the Su-Xi-Chang region. Two major points emerged from the results. First, the urban development of the Su-Xi-Chang metropolitan area has a relatively concentrated spatial distribution. When considering the local spatial correlation, there is a relatively large proportion of areas with H–H correlation and L–H correlation. The H–H correlation area is mainly concentrated in the central urban area of Suzhou and Wuxi, and Kunshan, which connects Suzhou and Shanghai. This forms a spatial concentration area with high urban development levels. The L–H correlation area is mainly concentrated in cities such as Yixing and Changshu. After the central city developed to a certain stage in 2010, the spatial agglomeration of small and medium-sized cities that lagged in size became more clear. The L–L agglomeration area is mainly concentrated in Liyang and Jintan, with a widening development gap from surrounding cities and counties. This has led to a development trend of marginalization. Second, the urbanization rate index had a weak driving effect on the evolution and development of urban spatial pattern.
PubDate: 2022-01-04
DOI: 10.1007/s10651-021-00523-2

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