Air pollution is one of the global problems of the current era. According to World Health Organization more than 80% of the people living in metropolitan areas breathe air which exceeds the guideline limits. Particulate matter, the mixture of liquid and solid particles having diameters less than 10 μm, is one of the important pollutants in the air. The main source of the Particulate matter is mostly

The availability of a variety of Global Climate Models (GCMs) has increased the importance of the selection of suitable GCMs for impact assessment studies. In this study, we have used Bayesian Model Averaging (BMA) for GCM(s) selection and ensemble climate projection from the output of thirteen CMIP5 GCMs for the Upper Indus Basin (UIB), Pakistan. The results show that the ranking of the top best models

The issue on whether food production has a severe impact on the environment has been receiving increased attention in recent years. By utilizing three different estimators, this paper investigates the effect of environmental degradation on food production underlying the Cobb–Douglas production function. We also test the role of R&D, capital and labour on food production. All three estimators provide

Misidentification of animals is a common problem for many capture-recapture experiments. Considerably misleading inference may be obtained when traditional models are used for capture-recapture data with misidentification. In this paper, we investigate the so-called band-read error model for modeling misidentification, assuming that it is possible to identify one marked individual as another on each

Detection heterogeneity is inherent to ecological data, arising from factors such as varied terrain or weather conditions, inconsistent sampling effort, or heterogeneity of individuals themselves. Incorporating additional covariates into a statistical model is one approach for addressing heterogeneity, but there is no guarantee that any set of measurable covariates will adequately address the heterogeneity

Recently there has been some renewed interest in skew-normal distribution (SND) because it provides a nice and natural generalization (in terms of accommodating skewed data) over the usual normal distribution. In this study we have used the SND error in a regression set-up, discussed a step by step approach on how to estimate all the model parameters, and show how naturally the resultant SND-based

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

In this article, we propose procedures based on the modified information criterion and the confidence distribution for detecting and estimating changes in a three-parameter Weibull distribution. Corresponding asymptotic results of the test statistic associated the detection procedure are established. Moreover, instead of only providing point estimates of change locations, the proposed estimation procedure

This paper proposes a green finance index that may help policymakers and investors take more favorable actions based on the development of green finance. After analysis and organization of the development process of green finance and related green finance and index concepts, this paper uses the improved fuzzy comprehensive evaluation method to construct a measurement model suitable for measuring the

Joint species distribution modeling is attracting increasing attention these days, acknowledging the fact that individual level modeling fails to take into account expected dependence/interaction between species. These joint models capture species dependence through an associated correlation matrix arising from a set of latent multivariate normal variables. However, these associations offer limited

This paper aims to explore the main determinants of environmental quality in Egypt by utilizing the data covering the years from 1971 to 2014. These dynamics were explored by utilizing the ARDL, wavelet coherence and Gradual shift causality approaches. The ARDL bounds test revealed cointegration among series. Findings based on the ARDL revealed; (i) positive and significant interaction between energy

The aim of this research is to determine the design criteria of habitable spaces with microclimate data for ecological urbanization. Different types of housing in the city of Erzurum, which is in the northeast region of Turkey, were used in this study. The hourly microclimate and air pollution data from 2018 for the city center were used to analyze the relationships between different residential textures

This paper examines the causal relationship between economic growth and environmental degradation for 115 countries over the period 1990–2016. The empirical results show a long-run equilibrium relationship between the CO2, CH4 and PM2.5 emissions and their macroeconomic determinants economic growth, energy consumption, trade openness, urbanization, and transportation. The author found mixed support

On October 4, 2015, the Cedar Creek gage at Congaree National Park stopped reporting stages, and the readings did not resume until approximately 2 weeks later because of record-breaking rainfall that led to some of the worst floodings in South Carolina history. Our goal is to reconstruct the Cedar Creek stage during this missing 2-week window. Our analysis uses a sample of ten historical flood events

Due to the nature of the distributed lag model, researchers are likely to encounter the problem of multicollinearity in this model. Biased estimation techniques, one of which is Almon ridge estimation, are alternatively considered instead of Almon estimation with the aim of recovering the multicollinearity. Although estimation performance is often taken into consideration, predictive performance is

The complexity of seismogenesis requires the development of stochastic models, the application of which aims to improve our understanding on the seismic process and the associated underlying mechanisms. Seismogenesis in the Corinth Gulf (Greece) is modeled through a Constrained-Memory Stress Release Model (CM-SRM), which combines the gradual increase of the strain energy due to continuous slow tectonic

The study reported in this article investigated the relationship between higher education and environmental sustainability with control variables including foreign direct investment, electricity consumption, population, and gross domestic product from 30 provinces in China during the 2000–2018 period. The data were analyzed with cross-sectional dependency tests, panel unit-root tests, Kao cointegration

The Neyman–Scott processes introduced so far assume a symmetric distribution for the positions of the offspring points and this makes them inappropriate for modelling the skewed and bimodal clustered patterns and is a hindrance in fitting them to data that exhibit skewness or bimodality. In this paper, we apply the bivariate alpha-skew-normal distribution to the locations of the offspring points and

Efficient energy production and distribution systems are urgently needed to reduce world climate change. Since modern district heating systems are sustainable energy distribution services that exploit renewable sources and avoid energy waste, in-depth knowledge of thermal energy demand, which is mainly affected by weather conditions, is essential to enhance heat production schedules. We hence propose

Evolution processes of multiple competitive and non-competitive species have traditionally been handled using different methods. In particular, evolution processes of multiple competitive species have usually been evaluated by the continuous and discrete proportions analysis; however, such evolution processes cannot be solely characterized by their relative proportions in practice. In this paper, we

Estimating bird and bat mortality at wind facilities typically involves searching for carcasses on the ground near turbines. Some fraction of carcasses inevitably lie outside the search plots, and accurate mortality estimation requires accounting for those carcasses using models to extrapolate from searched to unsearched areas. Such models should account for variation in carcass density with distance

The longitude, latitude and depth of the hypocenter in 3-D space and the date and time of rupture makes an earthquake a “point” in a spatio-temporal point pattern, observed over a region and months, years or decades. The magnitude of earthquakes marks the point pattern, as would hypocenter depth do if only the longitude and latitude of epicenters were used for location in 2-D space. Stochastic declustering

In this paper we present a detailed comparison of the prediction error based model selection criteria in circular random effects models. The study is primarily motivated by the need for an understanding of their performance in real life ecological and environmental applications. Prediction errors are based on posterior predictive distributions and the model selection methods are adjusted for the circular

This paper empirically explores the dynamic relationships between financial development, globalization, energy consumption, economic growth, and ecological footprint in G7 countries over the period 1980–2015. Using a recently introduced threshold cointegration test with an endogenous structural break, the paper aims primarily to determine the effects of financial development and globalization on environmental

We consider the problem of estimating the mean function from a pair of paleoclimatic functional data sets after one of them has been registered with the other. We establish that registering one data set with respect to the other is the appropriate way to formulate this problem. This is in contrast with estimation of the mean function on a ‘central’ time scale that is preferred in the analysis of multiple

This study examines the runoff prediction of each hydrometric station and each month in the mainstream of the Yellow River in China. From the perspective of functional data, the monthly runoff of each hydrometric station can be regarded as a function of both time and space. A sequence of such functions is formed by collecting the data over the years. We propose a new approach by combining the two-dimensional

Efforts are being made to contain rabies in Tanzania, reported in the southern highland regions, since 1954, and endemic in all districts in Tanzania currently. It has been determined that mass vaccination of at least \(70\%\) of a domestic animal population is most effective in reducing transmission of rabies. Current vaccination campaigns in Tanzanian villages have many administrative and logistical

The paper investigates the non-linear causality from energy consumption and economic growth to ecological footprint for the case of Turkey by employing ARDL Models and ECM-Based Granger Causality over the period from 1961 to 2016. The major contribution of the article to the literature is that (i) the data period of the empirical analysis of the study is much longer than the one of the other studies

Teleconnection, the strong dependence between two distant locations, provides interesting information for discovering the structures in spatial data. While teleconnections are often sparse and estimated through sample correlations, there are also abundant correlations among nearby locations. We propose a far-near covariance model that simultaneously models the abundant short-distance dependencies and

This paper introduces a time-varying parameter regression model for modeling, forecasting, and assessing inter-annual trends in daily snow depths. The time-varying parameter regression is written in a simple state-space representation and forecasted using a Kalman filter. The recursive Kalman filter algorithm updates the time-varying parameter sequentially when a new data point becomes available and

Management of large-scale pelagic fisheries relies heavily on fishery data to provide information on tuna population status because, for widely distributed populations, the cost of collecting survey data is often prohibitively high. However, fishery data typically do not provide direct information on interactions among fishing vessels, and thus methods of analysis often assume that vessels operate

This paper illustrates the use of diffusion kernels to estimate smooth density and regression functions defined on highly complex domains. We generalize the two-dimensional lattice-based estimators of Barry and McIntyre (2011) and McIntyre and Barry (2018) to estimate any function defined on a domain that may be embedded in \(\mathbb {R}^d\), \(d\ge 1\). Examples include function estimation on the

The presence and establishment of a tree species at a particular spatial location is influenced by multiple physiological and environmental filters such as propagule pressure (seed availability), light and moisture availability, and slope and elevation. However, a less understood environmental filter to species-specific establishment is competition or facilitation between dominant tree species. For

When working with contemporary spatial ecological datasets, statistical modellers are often confronted by two major challenges: (I) the need for covariance models with the flexibility to accommodate directional patterns of anisotropy; and (II) the computational effort demanded by high-dimensional inverse and determinant problems involving the covariance matrix \(\vec {V}\). In the case of rectangular

This paper presents an extension of the Geostatistical model under preferential sampling in order to accommodate possible local repulsion effects. This local repulsion can be caused by the researcher in charge of collecting data who, after observing the stochastic process of interest in a specific location, avoids collecting new samples near this place. Proceeding in this way, the resulting sampling

Flash floods have devastating power due to their high rainfall intensities and short durations, causing serious damage to infrastructure and human life. Recent observations in the desert environment of Kuwait witnessed an increase in rainfall extremes, and the currently adopted intensity–duration–frequency (IDF) curves constructed three decades ago did not produce reliable design frequencies for existing

Optimizing sustainable renewable energy portfolios is one of the most complicated decision making problems in energy policy planning. This process involves meeting the decision maker’s preferences, which can be uncertain, while considering several conflicting criteria, such as environmental, societal, and economic impact. In this paper, rather than using existing techniques, a novel multi-objective

We develop a new method for estimating population abundance for notoriously difficult to count populations. This is made possible using an easy to count auxiliary population with a known link to the target population under the framework of a layered hidden Markov model. We apply the new methods to estimate the breeding population of an Ancient Murrelet seabird colony, using Ancient Murrelet chicks

We investigate a framework for improving predictions from models for spatio-temporal data. The framework is based on minimising the mean squared prediction error and can be applied to many models. We applied the framework to a model for monthly rainfall data in the Murray-Darling Basin in Australia. Across a range of prediction situations, we improved the predictive accuracy compared to predictions

We propose a new extended regression model based on the logarithm of the generalized odd log-logistic Weibull distribution with four systematic components for the analysis of survival data. This regression model can be very useful and could give more realistic fits than other special regression models. We obtain the maximum likelihood estimates of the model parameters for censored data and address

In this paper we present a weighted mixture distribution component counts (MDCC) approach for estimating total number of species. The proposed method combines conditional estimates of component counts from several candidate mixture distributions and uses bootstrap for confidence interval estimation. The distribution specification is flexible and can be adjusted to suit a variety of datasets. Smoothing

We propose a spatiotemporal generalized von Bertalanffy (vonB) growth model that also includes between-individual (BI) variation and male/female correlation. The generalized vonB model includes the effect of maturation on growth. The model and the methodology are applied to a long time-series of survey observations of age and length for American plaice on the Grand Bank off the northeast coast of Canada

Few studies have attempted to study the environmental Kuznets curve (EKC) hypothesis at the individual sector level using more than one sector at once. This paper investigates the existence of the EKC hypothesis in the Iberian countries (Portugal and Spain) using thirteen economic activity sectors for each, analyzing each individual sector’s cointegration and causality relationships considering carbon

When investigating extremes of weather variables, it is seldom that a single weather station determines the damage, and extremes may be caused from the combined behaviour of several weather stations. To investigate joint dependence of extreme wind speed, a bivariate generalised extreme value distribution (BGEVD) was considered from frequentist and Bayesian approaches to analyse the extremes of component-wise

The statistical analysis of environmental data from remote sensing and Earth system simulations often entails the analysis of gridded spatio-temporal data, with a hypothesis test being performed for each grid cell. When the whole image or a set of grid cells are analyzed for a global effect, the problem of multiple testing arises. When no global effect is present, we expect \( \alpha \)% of all grid

Species distribution modelling (SDM) is a family of statistical methods where species occurrence/density/richness are combined with environmental predictors to create predictive spatial models of species distribution. However, it often turns out that due to complex multi-level interactions between predictors and the response function, different types of models can detect different numbers of important

Cetacean abundance estimation often relies on distance sampling methods using shipboard visual line-transect surveys, which assumes that all animals on the trackline are detected and that the detection of animals decreases with increasing distance from the trackline. Mark–Recapture Distance Sampling (MRDS) typically employs a secondary visual observation team and may be used to identify the fraction

The results show that a comprehensive evaluation system consisting of natural resources, pollution response, government management, and legal involvement can systematically assess the coupling coordination degree (CCD) of environmental governance. The information entropy weight (IEW) method and technique for order preference by similarity to an ideal solution (TOPSIS) are jointly used to analyze the

The paper proposes a new perspective in the environmental and resource economics literature by examining innovation (measured by R&D expenditures), FDI (measured by country–country technology transfer), and energy–environment–growth nexus. Using simultaneous equation modelling (SEMs), three econometric functions were formulated for production, energy consumption, and environmental pollution with GDP

For spatial linear regression, the traditional approach to capture spatial dependence is to use a parametric linear mixed-effects model. Spline surfaces can be used as an alternative to capture spatial variability, giving rise to a semiparametric method that does not require the specification of a parametric covariance structure. The spline component in such a semiparametric method, however, impacts

In this note we would like to point out that the lower bound estimator of the frequency of hidden units in a target population, developed by Chao in ecology, was developed independently in astro-physics and has been used to estimate the size of flare stars in the Pleiades.

For many clustered populations, the prior information on an initial stratification exists but the exact pattern of the population concentration may not be predicted. Under this situation, the stratified adaptive cluster sampling (SACS) may provide more efficient estimates than the other conventional sampling designs for the estimation of rare and clustered population parameters. For practical interest

To qualify niche space and niche overlap, the current methods are based on a strict assumption of the normal distribution and restrict dependence to the linear structure between resource axes. In this study, we propose a new method for measuring the stoichiometric niche space and niche overlap based on copula theory. Our method does not require the resource axes to obey the multivariate normal distribution

Functional data analysis tools, such as function-on-function regression models, have received considerable attention in various scientific fields because of their observed high-dimensional and complex data structures. Several statistical procedures, including least squares, maximum likelihood, and maximum penalized likelihood, have been proposed to estimate such function-on-function regression models

This paper develops statistical inference for population mean and total using stratified judgment post-stratified (SJPS) samples. The SJPS design selects a judgment post-stratified sample from each stratum. Hence, in addition to stratum structure, it induces additional ranking structure within stratum samples. SJPS is constructed from a finite population using either a with or without replacement sampling

Statistical packages such as edgeR and DESeq are intended to detect genes that are relevant to phenotypic traits and diseases. A few studies have also modeled the relationships between gene expressions and traits. In the presence of multicollinearity and outliers, which are unavoidable in genetic data, the robust ridge regression estimator can be applied with the trait value as the response variable

The mitigation of CO2 emissions requires a global effort with common but differentiated responsibilities. In this paper, we identify clusters of CO2 emissions across 72 countries. First, using the stochastic version of the IPAT and employing the dynamic common correlated effects technique, we identify three key determinants affecting CO2 emissions (non-renewables, population, and real GDP). In the

Model averaging is commonly used to allow for model uncertainty in parameter estimation. As well as providing a point estimate that is a natural compromise between the estimates from different models, it also provides confidence intervals with better coverage properties, compared to those based on a single best model. In recent years, the concept of a confidence distribution has been promoted as a

An important objective of experimental biology is the quantification of the relationship between sets of predictor and response variables, a statistical analysis often termed variance partitioning (VP). In this paper, a series of simulations is presented, aiming to generate quantitative estimates of the expected statistical uncertainty of VP analyses. We demonstrate scenarios with considerable uncertainty

A spatiotemporal calibration and resolution refinement model was fitted to calibrate nitrogen dioxide (\(\hbox {NO}_2\)) concentration estimates from the Community Multiscale Air Quality (CMAQ) model, using two sources of observed data on \(\hbox {NO}_2\) that differed in their spatial and temporal resolutions. To refine the spatial resolution of the CMAQ model estimates, we leveraged information using