Livingston, Glen, Jr.Allingham, DavidRayner, J. C. W.
17页
查看更多>>摘要：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.
查看更多>>摘要：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.
查看更多>>摘要：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), PM2.5 in particular, it raises a crucial public health concern, but quantifying PM2.5 accurately across large geographic areas and across time poses a great challenge. To investigate PM2.5 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 PM2.5 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 PM2.5 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.
查看更多>>摘要：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.
查看更多>>摘要：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.
查看更多>>摘要：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.
Russell, Brook T.Cressman, Kimberly A.Schmit, John PaulShull, Suzanne...
33页
查看更多>>摘要：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.
查看更多>>摘要：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.
查看更多>>摘要：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.
查看更多>>摘要：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.
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Springer Nature