Showing papers in "Environmental and Ecological Statistics in 2021"

The imperativeness of biomass energy consumption to the environmental sustainability of the United States revisited

Abstract: The predicament of increasing environmental issues in the last few decades has increased the interest in clean energy sources. Some recently created sources of energy, for example, biomass energy, may decrease environmental pressure. This study aimed to uncover the causality between biomass energy consumption (BEC) and carbon dioxide (CO2) emission in the United States (U.S.) using the bootstrap Granger full-sample and sub-sample rolling window estimates method for the period 1981M01 to 2019M12. A one-way relationship was indicated, from biomass energy consumption to CO2 emissions, using the Granger causality test. The durability of the estimated vector autoregressive (VAR) model has been calculated by considering the structural changes. The results show that BEC has both positive and negative effects on CO2 emissions in sub-samples, and CO2 emissions also show a causative relationship with biomass energy consumption. These outcomes can help policymakers consider biomass energy a perfect wellspring of energy to acquire environmental sustainability and energy security.

Determinants of CO2 emissions: empirical evidence from Egypt

Abstract: 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 usage and CO2 emissions; (ii) no evidence of significant link was found between urbanization and CO2 emissions; (iii) no significant link was found between gross capital formation and CO2 emissions; and (iv) GDP growth impact CO2 emissions positively in Egypt. Furthermore, findings from the wavelet coherence technique provide supportive evidence for the ARDL estimate. The Gradual shift causality test revealed one-way causality from CO2 emissions to energy consumption and economic growth, while there is evidence of feedback causality between CO2 and gross capital formation. Based on these findings, policymakers in Egypt need to formulate environmental policies to promote sustainable urbanization and clean energy without undermining economic growth.

Decoupling and decomposition analysis of environmental impact from economic growth: a comparative analysis of Pakistan, India, and China

Abstract: The dispute between economic growth and greenhouse gas emissions is one of the major challenges of the twenty-first century. The central issue of the emerging economies revolves around the decoupling of economic growth and the rising carbon dioxide (CO2) emissions. This study examines the decoupling the CO2 emissions from the economic growth through the employment of the Tapio decoupling index and decomposition of CO2 emissions into its pre-determined factors through the Log Mean Divisia Index (LMDI) decomposition technique for Pakistan, India, and China (PIC) for a time span of 1990–2014. The findings of the Tapio elasticity analysis depict that in a few years, environmental impact has been seen to be decoupled from the economic growth in the respective PIC countries. However, relatively Pakistan experienced expensive negative decoupling; India mostly experienced weak decoupling and expensive coupling, while China exhibited weak decoupling in multiple years. In addition, the analysis of Tapio decoupling elasticity showed that energy intensity is the key factor supporting the decoupling in PIC countries, while population, affluence (GDP per capita) and energy structure have weakened the progress of decoupling. Furthermore, the analysis of the LMDI decomposition suggested that population, energy structure and affluence in PIC countries increase the CO2 emissions, while energy intensity reduces CO2 emissions, while mixed effects are reflected by carbon intensity.

The measurement of green finance index and the development forecast of green finance in China

Abstract: 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 development level of green finance based on the principle of fuzzy mathematics. The index weight adopts the entropy method and improved Analytic Hierarchy Process (AHP) joint determination. At the same time, using the relevant statistical indicators of China's green credit from 2011 to 2019, and using the constructed model, the level of China's green finance development during this period was evaluated. Finally, the obtained data and classical gray model methods were used to predict China's green development level from 2020 to 2024. The research results show that: This model is a good measure of the level of development of green finance, and China's green finance index has generally shown a rapid growth trend over the past nine years, with the fastest growth rate between 2013 and 2014. From the perspective of the weight of each index affecting the green financial index, the weight of new energy, green transportation projects and new energy vehicles ranked in the top three, and the impact of these three indexes on China's green financial index is significant. In the future, China's green financial development level will continue to improve.

Measuring the impact of higher education on environmental pollution: new evidence from thirty provinces in China

Abstract: 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 tests, fully modified ordinary least squares, and dynamic ordinary least squares. Some of the main results are presented as follows. First, the results showed that higher education and foreign direct investment play a vital role in mitigating CO2 emissions, thereby confirming both the education-CO2 led hypothesis and the pollution halo hypothesis, respectively. Second, the estimates suggested that an increase in electricity consumption, population, and gross domestic product significantly contributed to enhancements in CO2 emissions. Based on the current estimated results, this research proposes important policies to help policymakers and governments in mitigating CO2 emissions.

Modeling spatially biased citizen science effort through the eBird database

Abstract: Citizen science databases are increasing in importance as sources of ecological information, but variability in effort across locations is inherent to such data. Spatially biased data—data not sampled uniformly across the study region—is expected. A further introduction of bias is variability in the level of sampling activity across locations. This motivates our work: with a spatial dataset of visited locations and sampling activity at those locations, we propose a model-based approach for assessing effort at these locations. Adjusting for potential spatial bias both in terms of sites visited and in terms of effort is crucial for developing reliable species distribution models (SDMs). Using data from eBird, a global citizen science database dedicated to avifauna, and illustrative regions in Pennsylvania and Germany, we model spatial dependence in both the observation locations and observed activity. We employ point process models to explain the observed locations in space, fit a geostatistical model to explain observation effort at locations, and explore the potential existence of preferential sampling, i.e., dependence between the two processes. Altogether, we offer a richer notion of sampling effort, combining information about location and activity. As SDMs are often used for their predictive capabilities, an important advantage of our approach is the ability to predict effort at unobserved locations and over regions. In this way, we can accommodate misalignment between point-referenced data and say, desired areal scale density. We briefly illustrate how our proposed methods can be applied to SDMs, with demonstrated improvement in prediction from models incorporating effort.

Measurement and spatial statistical analysis of green science and technology innovation efficiency among Chinese Provinces

Abstract: This paper measured the efficiency of green science and technology (S&T) innovation in 30 Chinese provinces from 2008 to 2017 by constructing a three-stage super-efficiency DEA model that contains undesired output and then analyzed the spatial performance for these provinces. The purpose is to calculate exactly the extent to which S&T innovation in different regions of China has contributed to economic development, excluding negative impacts on the ecological environment and any spatial differences that have emerged in the past decade. The results show that the overall performance of green S&T innovation efficiency in Chinese regions was poor in the past decade, and there is still much room for improvement. In addition, China's investment in S&T innovation and environmental management is inefficient and wasteful. From the temporal perspective, efficiency in green innovation shows a slowly increasing trend. From the spatial perspective, the efficiency shows a strict correlation with economic development, that is, an obvious three-level spatial distribution pattern of "east, middle, and west".

A hybrid CNN-LSTM model for predicting PM2.5 in Beijing based on spatiotemporal correlation

Abstract: Long-term exposure to air environments full of suspended particles, especially PM2.5, would seriously damage people's health and life (i.e., respiratory diseases and lung cancers). Therefore, accurate PM2.5 prediction is important for the government authorities to take preventive measures. In this paper, the advantages of convolutional neural networks (CNN) and long short-term memory networks (LSTM) models are combined. Then a hybrid CNN-LSTM model is proposed to predict the daily PM2.5 concentration in Beijing based on spatiotemporal correlation. Specifically, a Pearson's correlation coefficient is adopted to measure the relationship between PM2.5 in Beijing and air pollutants in its surrounding cities. In the hybrid CNN-LSTM model, the CNN model is used to learn spatial features, while the LSTM model is used to extract the temporal information. In order to evaluate the proposed model, three evaluation indexes are introduced, including root mean square error, mean absolute percent error, and R-squared. As a result, the hybrid CNN-LSTM model achieves the best performance compared with the Multilayer perceptron model (MLP) and LSTM. Moreover, the prediction accuracy of the proposed model considering spatiotemporal correlation outperforms the same model without spatiotemporal correlation. Therefore, the hybrid CNN-LSTM model can be adopted for PM2.5 concentration prediction.

The relationships between ecological urbanization, green areas, and air pollution in Erzurum/Turkey

Abstract: 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, air pollution, green area, and thermal comfort. The data of Ata Botanical Garden, where trees are dense, and the vicinity of the city center, where air pollution is most intense, are discussed. The physiological equivalent temperature (PET) and sky view factor (SVF) data were analyzed with a RayMan Pro 2.1 computer model. Spatial settlement area analyses were conducted by evaluating the SVF values in ArcGIS 10.3. The relationships between air pollution, residential textures, and SVF data were determined. A comparative analysis of existing green areas was undertaken with the pollution forecast maps. The statistical results indicated that there is a difference in the relationship between the thermal comfort and air pollution of the residential textures and the SVF value of the study area according to the seasons. A strong relationship was found in the present study between pollutants and SVF, while it is weaker for green areas. Air pollution was observed to have the lowest density in the areas where detached house types are located among the different residential textures. In addition, in the same area, street geometry is closer to its ideal form, and therefore thermal comfort is also at a higher level. As a result of this study, residential textures were found to have effects on air pollution and thermal comfort.

How to tackle plastic bags and bottles pollution crisis in Pakistan? A cost–benefit analysis approach

Abstract: Plastic pollution is among the many socio-economic and environmental dilemmas that have engulfed Pakistan. The exponentially increasing consumption of plastic and the difficulty of dealing with its waste has compelled the government to impose a ban on the use of non-biodegradable plastics. This ban has made Pakistan the 128th country to curb plastic usage through punitive measures. However, the country lacks cheap and sustainable alternatives for plastic bags and bottles, which form the most significant chunk of plastic waste. This research study aims to analyze and propose alternatives that can replace these plastic bags and bottles without compromising the lifestyle of citizens. The aim of this study is two-fold; first, it employs Fuzzy VIKOR, a Multi-Criteria Decision-Making technique for comparison and prioritization of alternatives, i.e., Conventional Plastic bags, Paper bags, and Bioplastic bags, with respect to multiple aspects of sustainability. Secondly, it performs a cost–benefit analysis of a Bioplastic plant, with a focus on the production of biodegradable plastic bottles. The MCDM analysis prioritized the bioplastic bags, followed by paper bags and the least preferable alternative turned out to be conventional plastic bags. The cost–benefit analysis indicated that although the production of bioplastic bottles instead of conventional plastic bottles would lead to the reduction of detrimental environmental impacts, however, currently it is not financially profitable for the industrialists to switch to bioplastics. Therefore, it is recommended that government authorities should incorporate carbon taxes and subsidize the sustainable development sector, which would, in turn, lead to the reduction of plastic consumption and waste in society.

Nexus between disaggregated electricity consumption and CO2 emissions in Turkey: new evidence from quantile-on-quantile approach

Abstract: Carbon emission is still one of the most hazardous environmental problem across the world. International authorities as well as local governments are projecting strategies to deal with this issue. To this end, our study aims to explore the link between energy consumption and carbon emission. We attempt to extend the current literature by identifying sector-specific impacts of electricity consumption on carbon emission. Findings suggest that the nexus between electricity consumption and CO2 is sector-specific as well as distribution specific in Turkey. In particular, we find a positive but weak impact of industrial electricity consumptions on CO2 emission. Yet, the effects get stronger for the lowest and highest quantiles of carbon emissions due industrial electricity consumption suggesting an alert for the shift in the current energy policies of the country. Furthermore, we find the electricity used in residents and transportation has insignificant effect on environmental degradation, in contrast to the commercial and public services electricity usage has a positive and strong effect on carbon emission. Therefore, controlling for distribution specific effects and disaggregated consumption patterns facilitate the efficient policy recommendations.

Bayesian non-parametric detection heterogeneity in ecological models

Abstract: 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, and the presence of unmodelled heterogeneity has been shown to produce biases in the resulting inferences. Other approaches for addressing heterogeneity include the use of random effects, or finite mixtures of homogeneous subgroups. Here, we present a non-parametric approach for modeling detection heterogeneity for use in a Bayesian hierarchical framework. We employ a Dirichlet process mixture which allows a flexible number of population subgroups without the need to pre-specify this number of subgroups as in a finite mixture. We describe this non-parametric approach, then consider its use for modeling detection heterogeneity in two common ecological motifs: capture–recapture and occupancy modeling. For each, we consider a homogeneous model, finite mixture models, and the non-parametric approach. We compare these approaches using simulation, and observe the non-parametric approach as the most reliable method for addressing varying degrees of heterogeneity. We also present two real-data examples, and compare the inferences resulting from each modeling approach. Analyses are carried out using the nimble package for R, which provides facilities for Bayesian non-parametric models.

Evaluation of CMIP5 models and ensemble climate projections using a Bayesian approach: a case study of the Upper Indus Basin, Pakistan

Abstract: 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 among thirteen GCMs is not uniform regarding maximum, minimum temperature, and precipitation. However, some models showed the best performance for all three variables. The selected GCMs were used to produce ensemble projections via BMA for maximum, minimum temperature and precipitation under RCP4.5 and RCP8.5 scenarios for the duration of 2011–2040. The ensemble projections show a higher correlation with observed data than individual GCM’s output, and the BMA’s prediction well captured the trend of observed data. Furthermore, the 90% prediction intervals of BMA’s output closely captured the extreme values of observed data. The projected results of both RCPs were compared with the climatology of baseline duration (1981–2010) and it was noted that RCP8.5 show more changes in future temperature and precipitation compared to RCP4.5. For maximum temperature, there is more variation in monthly climatology for the duration of 2011–2040 in the first half of the year; however, under the RCP8.5, higher variation was noted during the winter season. A decrease in precipitation is projected during the months of January and August under the RCP4.5 while under RCP8.5, decrease in precipitation was noted during the months of March, May, July, August, September, and October; however, the changes (decrease/increase) are higher than under the RCP4.5.

Nonparametric conditional density estimation in a deep learning framework for short-term forecasting

Abstract: Short-term forecasting is an important tool in understanding environmental processes. In this paper, we incorporate machine learning algorithms into a conditional distribution estimator for the purposes of forecasting tropical cyclone intensity. Many machine learning techniques give a single-point prediction of the conditional distribution of the target variable, which does not give a full accounting of the prediction variability. Conditional distribution estimation can provide extra insight on predicted response behavior, which could influence decision-making and policy. We propose a technique that simultaneously estimates the entire conditional distribution and flexibly allows for machine learning techniques to be incorporated. A smooth model is fit over both the target variable and covariates, and a logistic transformation is applied on the model output layer to produce an expression of the conditional density function. We provide two examples of machine learning models that can be used, polynomial regression and deep learning models. To achieve computational efficiency, we propose a case–control sampling approximation to the conditional distribution. A simulation study for four different data distributions highlights the effectiveness of our method compared to other machine learning-based conditional distribution estimation techniques. We then demonstrate the utility of our approach for forecasting purposes using tropical cyclone data from the Atlantic Seaboard. This paper gives a proof of concept for the promise of our method, further computational developments can fully unlock its insights in more complex forecasting and other applications.

The role of odds ratios in joint species distribution modeling

Abstract: 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 insight into realized dependence behavior between species at sites. We focus on presence/absence data using joint species modeling, which, in addition, incorporates spatial dependence between sites. For pairs of species selected from a collection, we emphasize the induced odds ratios (along with the joint occurrence probabilities); they provide a better appreciation of the practical dependence between species that is implicit in these joint species distribution modeling specifications. For any pair of species, the spatial structure enables a spatial odds ratio surface to illuminate how dependence varies over the region of interest. We illustrate with a dataset from the Cape Floristic Region of South Africa consisting of more than 600 species at more than 600 sites. We present the spatial distribution of odds ratios for pairs of species that are positively correlated and pairs that are negatively correlated under the joint species distribution model.

Prediction framework in a distributed lag model with a target function: an application to global warming data

Abstract: 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 rarely handled in the distributed lag model. The principal purpose of this paper is to investigate the predictive performance of the distributed lag model through target function. In this context, we employ Almon ridge estimation to define a new predictor that is more resistant to multicollinearity. For an extensive analysis of the prediction problem in the distributed lag model, we concentrate on the theoretical results and comparisons. Then, the issue of determining optimal parameters is considered by means of minimizing the prediction mean square error. Numerical analysis depending on global warming data is examined to validate our theoretical outcomes. Moreover, a Monte Carlo experiment is carried out to evaluate the predictive ability of the estimators.

Is food production vulnerable to environmental degradation? A global analysis

Abstract: 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 consistent results using a panel of 53 countries for the period 1996–2017. First, CO2 emissions are harmful to food production. Second, both capital and R&D are found to have a positive relationship with food production. Meanwhile, an increase in labour tends to reduce food production. Furthermore, the Dumitrescu–Hurlin (DH) panel Granger causality test reveals that there is bidirectional causality between (i) food production and CO2 emissions, (ii) R&D and food production. The findings of our study not only contribute significantly to the existing literature but also bring about a better understanding on the pollution-food production nexus. Based on our findings, policies aimed at reducing CO2 emissions and stimulating R&D efforts are recommended.

Revisiting the impacts of economic growth on environmental degradation: new evidence from 115 countries

Abstract: 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 of the Environmental Kuznets Curve (EKC) hypothesis, confirming the U-shaped EKC for all the income countries in CO2 and an inverted U-shaped EKC both in CH4 and PM2.5 emissions for the low, lower-middle and high-income countries. In the subsequent Granger causality test, the author revealed that energy consumption and economic growth raise the level of CO2, the most significant pollutant because of their positive causal effect. Moreover, the impulse response function forecasts an inverted U-shaped EKC mostly for selected pollutants in all countries. Results suggest that promoting energy efficiency and reducing the use of fossil fuels are effective measures for reversing environmental degradation in the country.

The improved coupling coordination analysis on the relationship between climate, eco-environment, and socio-economy

Abstract: As a special socio-ecosystem, urban sustainability has been challenged by frequent human activities and natural disasters. Including climate into the socio-ecosystem evaluation framework, this paper constructed the climate, eco-environmental, and socio-economic evaluation index, respectively, to evaluate the core cities’ socio-ecology in China’s three economic circles. An improved entropy-TOPSIS method was subsequently employed to identify the contribution made by each indicator of the compound system, and a modified ternary Coupling Coordination Degree (CCD) model was developed to probe into the CCD levels among the three systems during the study. The results showed that: (1) The climate system’s composite scores were characterized by inter-annual fluctuation without a time trend. However, the system’s risk increased, manifesting in an increased probability of extreme weather events, especially in Shanghai. (2) The eco-environmental system witnessed an enormous stride, rising above the level of socio-economic development after 2007. Besides, the gap between the eco-environment and the socio-economy was gradually enlarged since 2014. (3) The eco-environment made the most contribution to the CCD’s improvement, meaning enhancing the eco-environmental performance was of paramount significance. The findings can help the government formulate more effective measures to balance climate, eco-environment, and socio-economy to achieve sustainable urban development.

High spatial resolution IoT based air PM measurement system

Abstract: 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 burning reactions associated with industry, vehicles and homes. Several studies have shown the lethal impact of particulate matter to public health and environment. The rise of particulate matter amount in air has been linked to several health problems such as not only respiratory diseases but also mortality in infants and heart attacks. Currently, bulky stations which are high-cost and have limited spatial resolution are used to monitor the air quality. In this study we developed an alternative particulate matter measurement system which is portable and low-cost (less than 200 USD) and also integrated with cloud computing. The system allows real time distant monitoring of PM particles with high spatial resolution (meter range). The developed sensor system is able to provide air quality data in correlation with the existing stations (R2 = 0.87). The statistical comparison between the developed system and the reference methods revealed that two systems produced statistically equal results in detecting the variations of the particulate matter.

Precision as a metric for acoustic survey design using occupancy or spatial capture-recapture

Abstract: Passive acoustic surveys provide a convenient and cost-effective way to monitor animal populations, and methods for conducting and analysing such surveys are undergoing rapid development. However, no standard metric exists to evaluate the proposed changes. Furthermore, the metrics that are commonly used are specific to a single stage of the survey workflow, and may not reflect the overall effects of a design choice. Here, we attempt to define the effectiveness of acoustic surveys conducted in two common frameworks of population inference—occupancy modelling and spatially explicit capture-recapture (SCR). Specifically, we investigate precision as a possible metric of survey performance, but we observe that it does not lead to generally optimal designs in occupancy modelling. In contrast, the precision of the SCR density estimate can be optimised with fewer experiment-specific parameters. We illustrate these issues using simulations. We further demonstrate how SCR precision can be used to evaluate design choices on a field survey of little spotted kiwi (Apteryx owenii). We compare call recognition by software and human experts. The resulting tradeoff between missed calls and faster data throughput was accurately captured with the proposed metric, while common metrics failed to identify optimal improvements and could be inflated by deleting data. Due to the flexibility of SCR framework, the approach presented here can be applied to a wide range of different survey designs. As the precision is directly related to the power of subsequent inference, this metric evaluates design choices at the application level and captures tradeoffs that are missed by stage-specific metrics, enabling reliable comparison of survey methods.

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.

Consequences of violating assumptions of integrated population models on parameter estimates

Abstract: While ecologists know that models require assumptions, the consequences of their violation become vague as model complexity increases. Integrated population models (IPMs) combine several datasets to inform a population model and to estimate survival and reproduction parameters jointly with higher precision than is possible using independent models. However, accuracy actually depends on an adequate fit of the model to datasets. We first investigated bias of parameters obtained from integrated population models when specific assumptions are violated. For instance, a model may assume that all females reproduce although there are non-breeding females in the population. Our second goal was to identify which diagnostic tests are sensitive to detect violations of the assumptions of IPMs. We simulated data mimicking a short- and a long-lived species under five scenarios in which a specific assumption is violated. For each simulated scenario, we fitted an IPM that violates the assumption (simple IPM) and an IPM that does not violate each specific assumption. We estimated bias and uncertainty of parameters and performed seven diagnostic tests to assess the fit of the models to the data. Our results show that the simple IPM was quite robust to violation of many assumptions and only resulted in small bias of the parameter estimates. Yet, the applied diagnostic tests were not sensitive to detect such small bias. The violation of some assumptions such as the absence of immigrants resulted in larger bias to which diagnostic tests were more sensitive. The parameters informed by the least amount of data were the most biased in all scenarios. We provide guidelines to identify misspecified models and to diagnose the assumption being violated. Simple models should often be sufficient to describe simple population dynamics, and when data are abundant, complex models accounting for specific processes will be able to shed light on specific biological questions.

Modified information criterion for regular change point models based on confidence distribution

Abstract: 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 provides the confidence sets for change locations at a given significance level through the confidence distribution. In general, the proposed procedures are valid for a large class of parametric distributions under Wald conditions and the certain regularity conditions being satisfied. Simulations are conducted to investigate the performance of the proposed method in terms of powers, coverage probabilities and average lengths of confidence sets with respect to a three-parameter Weibull distribution. Corresponding comparisons are also made with other existing methods to indicate the advantages of the proposed method. Rainfall data is used to illustrate the application of the proposed method.

Bayesian predictive model selection in circular random effects models with applications in ecological and environmental studies

Abstract: 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 manifold. Plug-in estimators of the circular distance parameters are also considered. A Monte Carlo experiment scheme taking the account of various realistic ecological and biological scenarios is designed. We introduced a coefficient that is based on conditional expectations to examine how the deviation from von Mises (vM) distribution, the standard choice in applications, effects the performances. Our results show that the performances of widely used circular predictive model selection criteria mostly depend on the sample size as well as within-sample-correlation. The approaches and selection strategies are then applied to investigate orientational behaviour of Talitrus saltator under the risk of dehydration and direction of wind with respect to associated atmoshperic variables.

Regression model under skew-normal error with applications in predicting groundwater arsenic level in the Mekong Delta Region

Abstract: 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 regression model can lead to a superior fitting to a given dataset. This generalization enhances the precision in predicting the future value of the response variable when the values of the independent (or input) variables are available. We validate the applicability of our proposed SND-based regression model by using a recently acquired dataset from the Mekong Delta Region (MDR) of Vietnam which had necessitated this study from a public health perspective. Using the existing survey data our proposed model allows all the stakeholders to better predict the groundwater arsenic level at a site easily, based on its geographic characteristics, in lieu of costly chemical analyses, which can be very beneficial to developing countries due to their resource constraints.

Analysing the relationship between district heating demand and weather conditions through conditional mixture copula

Abstract: 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 a mixture copula-based approach to investigate the complex relationship between meteorological variables, such as outdoor temperature and solar radiation, and thermal energy demand in the district heating system of the Italian city Bozen-Bolzano. We analyse data collected from 2014 to 2017, and estimate copulas after removing serial dependence in each time series using autoregressive integrated moving average models. Due to complex relationships, a mixture of an unstructured Student-t and a flipped Clayton copula is deemed the best model, as it allows differentiating the magnitude of dependence in each tail and exhibiting both heavy-tailed and asymmetric dependence. We derive the conditional copula-based probability function of thermal energy demand given meteorological variables, and provide useful insight on the production management phase of local energy utilities.

Halton iterative partitioning master frames

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.

A new probability model for modeling of strength of carbon fiber data: properties and applications

Abstract: The procedures to discover proper new models in probability theory for different data collections are highly prevalent these days among the researchers of this area whenever existing literature models are not appropriate. Before delivering a product, manufacturers of raw materials or finished materials must follow some compliance standards in various engineering disciplines to avoid severe losses. Materials of high strength are necessary to ensure the safety of human lives along with infrastructures to elude the significant obligations linked with the provisions of non-compliant products. Using probability theory, we introduce the weighted version of inverted Kumaraswamy Distribution, which could be considered a better model than some other sub-models used to model Carbon fiber’s strength data. We derive various statistical properties of this distribution such as cumulative distribution, moments, mean residual life, reversed residual life functions, moment generating function, characteristic function, harmonic mean, and geometric mean. Parameters are estimated through the maximum likelihood method and ordinary moments. Simulation studies are carried out to illustrate the theoretical results of these two approaches. Furthermore, two real data sets of Carbon fibers strength are utilized to contrast the proposed model and its sub-models like inverted Kumaraswamy distribution and Kumaraswamy Sushila distribution through different goodness of fit criteria such as Akaike Information Criterion (AIC), corrected Akaike information criterion, and the Bayesian Information Criterion (BIC). Results reveal the outperformance of the proposed model compared to other models, which render it a proper interchange of the current sub-models.

Space–time calibration of wind speed forecasts from regional climate models

Abstract: Numerical weather predictions (NWPs) are systematically subject to errors due to the deterministic solutions used by numerical models to simulate the atmosphere. Statistical postprocessing techniques are widely used nowadays for NWP calibration. However, time-varying bias is usually not accommodated by such models. The calibration performance is also sensitive to the temporal window used for training. This paper proposes space–time models that extend the main statistical postprocessing approaches to calibrate NWP model outputs. Trans-Gaussian random fields are considered to account for meteorological variables with asymmetric behavior. Data augmentation is used to account for the censoring of the response variable. The benefits of the proposed extensions are illustrated through the calibration of hourly 10-m height wind speed forecasts in Southeastern Brazil coming from the Eta model.