Estimating the provincial environmental Kuznets curve in China: a geographically weighted regression approach

This study estimates the environmental Kuznets curve (EKC) relationship at the province level in China. We apply empirical methods to test three industrial pollutants—SO₂ emission, wastewater discharge, and solid waste production—in 29 Chinese provinces in 1994–2010. We use the geographically weighted regression (GWR) approach, wherein the model can be fitted at each spatial location in the data, weighting all observations by a function of distance from the regression point. Hence, considering spatial heterogeneity, the EKC relationship can be analyzed region-specifically through this approach, rather than describing the average relationship over the entire area examined. We also investigate the spatial stratified heterogeneity to verify and compare risk factors that affect regional pollution with statistical models. This study finds that the GWR model, aimed at considering spatial heterogeneity, outperforms the OLS model; it is more effective at explaining the relationships between environmental performance and economic growth in China. The results indicate a significant variation in the existence of the EKC relationship. Such spatial patterns suggest province-specific policymaking to achieve balanced growth in those provinces.     

Analyzing explanatory factors of urban pluvial floods in Shanghai using geographically weighted regression

In the context of climate change and rapid urbanization, urban pluvial floods pose an increasing threat to human wellbeing and security in the cities of China. A valuable aid to managing this problem lies in understanding the roles of environmental factors in influencing the occurrence of pluvial floods. This study presents a spatial analysis of records of inundated streets in the inner city of Shanghai during 1997–2013. A geographically weighted regression (GWR) is employed to examine the spatially explicit relationships between inundation frequency and spatial explanatory factors, and an ordinary least squares regression (OLS) is used to validate the GWR results. Results from the GWR model show that the inundation frequency is negatively related to elevation, pipeline density, and river density, and is positively related to road/square ratio and shantytown ratio. The green ratio is another significant explanatory factor for inundation frequency, and its coefficients range from ?1.11 to 0.81. In comparison with the OLS model, the GWR model has better performance as it has higher R², and lower corrected Akaike information criterion and mean square error values, as well as insignificant spatial autocorrelation of the model residuals. Additionally, the GWR model reveals detailed site-specific roles of the related factors in influencing street inundation. These findings demonstrate that the GWR model is a useful tool for investigating spatially explicit causes of disasters. The results also provide guidance for policy makers aiming to mitigate urban pluvial flood risks.     

Assessing the relationships between elevation and extreme precipitation with various durations in southern Taiwan using spatial regression models

A spatially autocorrelated effect exists in precipitation of a mountainous basin. This study examines the relationship between maximum annual rainfall and elevation in the Kaoping River Basin of southern Taiwan using spatial regression models (i.e. geographically weighted regression (GWR), simultaneous autoregression (SAR), and conditional autoregression (CAR)). Results show that the GWR, SAR, and CAR models can improve spatial data fitting and provide an enhanced estimation for the rainfall–elevation relationship than the ordinary least squares approach. In particular, GWR achieves the most accurate estimation, and SAR and CAR achieve similar performance in terms of the Akaike information criterion. The relationship between extreme rainfall and elevation for longer duration is more concise than that for short durations. Results show that the spatial distribution of precipitation depends on elevation and that rainfall patterns in study area are heterogeneous between the southwestern plain and the eastern mountain area. Copyright © 2011 John Wiley & Sons, Ltd.     

中国区域地理、地形因子对降水分布影响的估算和分析

不同与以往基于最小二乘的多元线性回归方法,本文首次尝试将新型的第二代回归分析方法——偏最小二乘回归分析方法应用到中国区域的降水建模中．利用区域内394个气象观测站建站到2000年45年(及以上)的降水资料,建立了一个简单的年、季降水量和地理、地形因子(包括纬度、经度、地形高程、坡度、坡向和遮蔽度)的关系模型,估算了区域降水量中地理、地形的影响部分,并分析了这种影响的特征．结果表明,用此方法建立的模型能够解释70%以上的因变量的变异,相关系数基本都在0.84以上,经交叉有效性检验,模型的回归效果较显著．分析表明,在多元线性回归不适用的情况下,本文基于偏最小二乘法的简单模型能够比较准确地定性、定量地再现实际降水分布．     

The British way in water

This paper describes, and seeks to assess the effectiveness of, the institutional arrangements adopted in 1989 to permit the privatisation of water and waste water services in England and Wales by way of complete disposal of the operating assets of the former regional water authorities. It refers to the different arrangements in Scotland and Northern Ireland. It concentrates in particular, however, on the safety, environmental and economic regulatory arrangements in England and Wales and argues that they have been key factors in ensuring that profit-making operation of strong natural monopolies delivers long-term benefits for consumers and the environment. It concludes with an assessment of incentive economic regulation and a discussion of the controversial aspects of the performance of the system, including the merits of the RPI±X% regulation in the case of water.     

Modeling determinants of urban growth in Dongguan,China: a spatial logistic approach

This paper examines spatial variations of urban growth patterns in Chinese cities through a case study of Dongguan, a rapidly industrializing city characterized by a bottom-up pattern of development based on townships. We have employed both non-spatial and spatial logistic regression models to analyze urban land conversion. The non-spatial logistic regression has found the significance of accessibility, neighborhood conditions and socioeconomic factors for urban development. The logistic regression with spatially expanded coefficients significantly improves the orthodoxy logistic regression with lower levels of spatial autocorrelation of residuals and better goodness-of-fit. More importantly, the spatial logistic model reveals the spatially varying relationship between urban growth and its underlying factors, particularly the local influence of environment protection and urban development policies. The results of the spatial logistic model also provide clear clues for assessing environmental risks to take the local contexts into account.     

Regional and temporal characteristics of <Emphasis Type="Italic">bovine tuberculosis</Emphasis> of cattle in Great Britain

Bovine tuberculosis (TB) is a chronic disease in cattle that causes a serious food security challenge to the agricultural industry in terms of dairy and meat production. Spatio-temporal disease analysis in terms of time trends and geographic disparities of disease dynamics can provide useful insights into the overall efficiency of control efforts as well as the relative efficiency of different management measures towards eradication. In GB, Scotland has had a risk based surveillance testing policy under which high risk herds are tested frequently, and in September 2009 was officially declared as TB free. Wales have had an annual or more frequent testing policy for all cattle herds since January 2010, while in England several herds are still tested every 4 years except some high TB prevalence areas where annual testing is applied. Time series analysis using publicly available data for total tests on herds, total cattle slaughtered, new herd incidents, and herds not TB free, were analysed globally for GB and locally for the constituent regions of Wales, Scotland, West, North, and East England. After detecting trends over time, underlying regional differences were compared with the testing policies in the region. Total cattle slaughtered are decreasing in Wales, Scotland and West England, but increasing in the North and East English regions. New herd incidents, i.e., disease incidence, are decreasing in Wales, Scotland, West English region, but increasing in North and East English regions. Herds not TB free, i.e., disease prevalence, are increasing in West, North, and East English regions, while they are decreasing in Wales and Scotland. Total cattle slaughtered were positively correlated with total tests in the West, North, and East English regions, with high slopes of regression indicating that additional testing is likely to facilitate the eradication of the disease. There was no correlation between total cattle slaughtered and total tests on herds in Wales indicating that herds are tested frequent enough in order to detect all likely cases and so control TB. The main conclusion of the analysis conducted here is that more frequent testing is leading to lower TB infections in cattle both in terms of TB prevalence as well as TB incidence.     

Analyzing spatial ecological data using linear regression and wavelet analysis

Spatial (two-dimensional) distributions in ecology are often influenced by spatial autocorrelation. In standard regression models, however, observations are assumed to be statistically independent. In this paper we present an alternative to other methods that allow for autocorrelation. We show that the theory of wavelets provides an efficient method to remove autocorrelations in regression models using data sampled on a regular grid. Wavelets are particularly suitable for data analysis without any prior knowledge of the underlying correlation structure. We illustrate our new method, called wavelet-revised model, by applying it to multiple regression for both normal linear models and logistic regression. Results are presented for computationally simulated data and real ecological data (distribution of species richness and distribution of the plant species Dianthus carthusianorum throughout Germany). These results are compared to those of generalized linear models and models based on generalized estimating equations. We recommend wavelet-revised models, in particular, as a method for logistic regression using large datasets.     

Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor

In this study, we link and compare the geographically weighted regression (GWR) model with the kriging with an external drift (KED) model of geostatistics. This includes empirical work where models are performance tested with respect to prediction and prediction uncertainty accuracy. In basic forms, GWR and KED (specified with local neighbourhoods) both cater for nonstationary correlations (i.e. the process is heteroskedastic with respect to relationships between the variable of interest and its covariates) and as such, can predict more accurately than models that do not. Furthermore, on specification of an additional heteroskedastic term to the same models (now with respect to a process variance), locally-accurate measures of prediction uncertainty can result. These heteroskedastic extensions of GWR and KED can be preferred to basic constructions, whose measures of prediction uncertainty are only ever likely to be globally-accurate. We evaluate both basic and heteroskedastic GWR and KED models using a case study data set, where data relationships are known to vary across space. Here GWR performs well with respect to the more involved KED model and as such, GWR is considered a viable alternative to the more established model in this particular comparison. Our study adds to a growing body of empirical evidence that GWR can be a worthy predictor; complementing its more usual guise as an exploratory technique for investigating relationships in multivariate spatial data sets.     

Understanding airborne radiometric survey signals across part of eastern England

A low‐level airborne radiometric survey provides data on the concentrations of gammaemitting elements including potassium (K), thorium (Th) and uranium (U) in the upper half metre of the soil. Where weathering has not penetrated much beyond this depth, as in the young soils that cover much of England and Wales, the signal is likely to be related to the soil's clay content and its parent material. In these situations radiometric survey could be valuable for mapping soil digitally. We wished to understand how the radiometric signal relates to parent material and soil geochemistry, and to identify the spatial structure, if not the sources, of any unexplained variation. We analysed the joint spatial variation of the airborne gamma signal and high‐resolution soil geochemical survey data across part of eastern England by modelling their coregionalization. We also used reml to assess the joint effects of soil geochemistry and parent material on the radiometric signals of K and Th. The overall correlations of radiometric estimates with soil survey data for K and Th were large, as were the structural correlations for components of variation spatially dependent up to 49 and 16 km for K and Th respectively. This suggests that the radiometric signals for these two elements provide effective estimates of the amounts in the soil and their patterns of distribution. Although class of parent material accounted for a third of of the variance in the radiometric K signal, much of the variation within the classes is explained by geochemistry, suggesting that subtler changes can be detected. A larger proportion of the Th signal was accounted for by parent material. This supports our expectation that radiometric signals for K and Th provide information on parent material in the young landscapes of England and Wales. We are therefore confident that airborne radiometric surveys would be useful for making thematic maps of soil, particularly the soil's texture and closely related properties across England and Wales. Copyright © 2007 Natural Environment Research Council. Published in 2007 by John Wiley & Sons, Ltd.     

Estimation of rainfall intensity–duration–frequency curves at ungauged locations using quantile regression methods

Intensity–duration–frequency (IDF) curves of extreme rainfall are used extensively in infrastructure design and water resources management. In this study, a novel regional framework based on quantile regression (QR) is used to estimate rainfall IDF curves at ungauged locations. Unlike standard regional approaches, such as index-storm and at-site ordinary least-squares regression, which are dependent on parametric distributional assumptions, the non-parametric QR approach directly estimates rainfall quantiles as a function of physiographic characteristics. Linear and nonlinear methods are evaluated for both the regional delineation and IDF curve estimation steps. Specifically, delineation by canonical correlation analysis (CCA) and nonlinear CCA (NLCCA) is combined, in turn, with linear QR and nonlinear QR estimation in a regional modelling framework. An exhaustive comparative study is conducted between standard regional methods and the proposed QR framework at sites across Canada. Overall, the fully nonlinear QR framework, which uses NLCCA for delineation and nonlinear QR for estimation of IDF curves at ungauged sites, leads to the best results.     

Effect of coastline configuration on precipitation distribution in coastal zones

The effects of coastline, together with the other geographical and topographical factors such as longitude, latitude, elevation and distance from sea, on mean annual precipitation are investigated for the coastal area of the eastern Black Sea region. In this location, mountains run parallel to the sea coast. The coastline configuration is represented by a new variable, the coastline angle. A multiple linear regression (MLR) analysis is performed on mean annual precipitation recorded in coastal and inland gauges. In the study area, three variables—longitude, latitude and coastline angle—can explain 81% of the spatial variability of precipitation for the coastal gauges and 93% of that for the inland gauges. When the entire study area considered, without grouping the gauges, 80% of the spatial variability is explained by these variables. Copyright © 2009 John Wiley & Sons, Ltd.     

A broad‐scale assessment of the effect of improved soil management on catchment baseflow index

Although the importance of sustainable soil management is recognized, there are many threats to soils including widespread soil structural degradation. This reduces infiltration through the soil surface and/or the percolation of water through the soil profile, with important consequences for crop yields, nutrient cycling and the hydrological response of catchments. This article describes a broad‐scale modelling approach to assess the potential effect that improved agricultural soil management, through reduced soil structural degradation, may have on the baseflow index (BFI) of catchments across England and Wales. A daily soil–water balance model was used to simulate the indicative BFI of 45 696 thirty‐year model runs for different combinations of soil type, soil/field condition, land cover class and climate which encapsulate the variability across England and Wales. The indicative BFI of catchments was then calculated by upscaling the results by spatial weighting. WaSim model outputs of indicative BFI were within the 95% confidence intervals of the national‐average BFI values given for the Hydrology of Soil Type (HOST ? ) classes for 26 of the 28 classes. At the catchment scale, the concordance correlation coefficient between the BFI from the WaSim model outputs and those derived from HOST was 0·83. Plausible improvements in agricultural soil/field condition produced modest simulated increases of up to 10% in the indicative BFI in most catchments across England and Wales, although for much of southern and northern England the increases were less than 5%. The results suggest that improved soil management might partially mitigate the expected adverse effects of climate change on baseflow to rivers. Healthy, well‐functioning soils produce many additional benefits such as better agricultural yields and reduced pollutant movement, so improved soil management should provide win‐win opportunities for society, agricultural systems and the environment and provide resilience to some of the expected environmental impacts of climate change. Copyright © 2011 John Wiley & Sons, Ltd.     

Regional differences of urbanization in China and its driving factors

After more than 30 years of rapid urbanization, the overall urbanization rate of China reached 56.1% in 2015.However, despite China's rapid increase in its overall rate of urbanization, clear regional differences can be observed. Furthermore, inadequate research has been devoted to in-depth exploration of the regional differences in China's urbanization from a national perspective, as well as the internal factors that drive these differences. Using prefecture-level administrative units in China as the main research subject, this study illustrates the regional differences in urbanization by categorizing the divisions into four types based on their urbanization ratio and speed(high level: low speed; high level: high speed; low level: high speed; and low level: low speed). Next, we selected seven economic and geographic indicators and applied an ordered logit model to explore the driving factors of the regional differences in urbanization. A multiple linear regression model was then adopted to analyze the different impacts of these driving factors on regions with different urbanization types. The results showed that the regional differences in urbanization were significantly correlated to per capita GDP, industry location quotients, urban-rural income ratio,and time distance to major centers. In addition, with each type of urbanization, these factors were found to have a different driving effect. Specifically, the driving effect of per capita GDP and industry location quotients presented a marginally decreasing trend, while main road density appeared to have a more significant impact on cities with lower urbanization rates.     

Difficulties in the application of magnetic field gradient analysis to induction studies

Geomagnetic variation data recorded across an array of magnetometer stations in northern England have been used to compute an inductive response function. The function, if successfully determined, is appropriate for sounding vertical conductivity structures on a regional scale. The method employed is the horizontal spatial gradient method using station averaging and a linear approximation. It is found that, for the data under consideration, local and lateral anomalous fields exist in all three components at periods ranging from 10 to 10⁴ s. The presence of such anomalous fields invalidates the procedures used to estimate the regional field, whose characteristics are required to determine successfully the inductive response function.     

Predicting river width,depth and velocity at ungauged sites in England and Wales using multilevel models

Using a dataset of gauged river discharges taken from sites in England and Wales, linear multilevel models (also known as mixed effects models) were applied to quantify the variability in discharge and the discharge‐hydraulic geometry relationships across three nested spatial scales. A jackknifing procedure was used to test the ability of the multilevel models to predict hydraulic geometry, and therefore width, mean depth and mean velocity, at ungauged stations. These models provide a framework for making predictions of hydraulic geometry parameters, with associated levels of uncertainty, using different levels of data availability. Results indicate that as one travels downstream along a river there is greater variability in hydraulic geometry than is the case between rivers of similar sizes. This indicates that hydraulic geometry (and therefore hydrology) is driven by catchment area, to a greater extent than by natural geomorphological variations in the streamwise direction at the mesoscale, but these geomorphological variations can still have a major impact on channel structure. Copyright © 2008 John Wiley & Sons, Ltd.     

River temperature regimes of England and Wales: spatial patterns,inter‐annual variability and climatic sensitivity

Identification of the most sensitive hydrological regions to a changing climate is essential to target adaptive management strategies. This study presents a quantitative assessment of spatial patterns, inter‐annual variability and climatic sensitivity of the shape (form) and magnitude (size) of annual river/stream water temperature regimes across England and Wales. Classification of long‐term average (1989–2006) annual river (air) temperature regime dynamics at 88 (38) stations within England and Wales identified spatially differentiable regions. Emergent river temperature regions were used to structure detailed hydroclimatological analyses of a subset of 38 paired river and air temperature stations. The shape and magnitude of air and water temperature regimes were classified for individual station‐years; and a sensitivity index (SI, based on conditional probability) was used to quantify the strength of associations between river and air temperature regimes. The nature and strength of air–river temperature regime links differed between regions. River basin properties considered to be static over the timescale of the study were used to infer modification of air–river temperature links by basin hydrological processes. The strongest links were observed in regions where groundwater contributions to runoff (estimated by basin permeability) were smallest and water exposure time to the atmosphere (estimated by basin area) was greatest. These findings provide a new large‐scale perspective on the hydroclimatological controls driving river thermal dynamics and, thus, yield a scientific basis for informed management and regulatory decisions concerning river temperature within England and Wales. © 2013 The Authors. Hydrological Processes published by John Wiley & Sons, Ltd.     

Local spatial regression models: a comparative analysis on soil contamination

Spatial data analysis focuses on both attribute and locational information. Local analyses deal with differences across space whereas global analyses deal with similarities across space. This paper addresses an experimental comparative study to analyse the spatial data by some weighted local regression models. Five local regression models have been developed and their estimation capacities have been evaluated. The experimental studies showed that integration of objective function based fuzzy clustering to geostatistics provides some accurate and general models structures. In particular, the estimation performance of the model established by combining the extended fuzzy clustering algorithm and standard regional dependence function is higher than that of the other regression models. Finally, it could be suggested that the hybrid regression models developed by combining soft computing and geostatistics could be used in spatial data analysis.     

Suitability of satellite-based hydro-climate variables and machine learning for streamflow modeling at various scale watersheds

ABSTRACT

Streamflow modeling is essential to investigate processes in the hydrologic cycle and important for water resource management application. However, in-situ hydrologic data paucity, because of various factors such as economic, political, instrument malfunctioning, and poor spatial distribution, makes the modeling process challenging. To overcome this limitation, we introduced a satellite remote sensing-based machine learning approach – boosted regression tree (BRT) – that integrates spatial land surface and climate variables that describe the sub-units, and applied it in three variable size watersheds in the Upper Mississippi River Basin (UMRB), USA. The model simulation results were tested using an independent dataset and showed Nash–Sutcliffe efficiency values of 0.80, 0.76, and 0.69 for the UMRB, Illinois River Watershed, and Raccoon River Watershed, respectively. In addition, we compared the performance of the machine learning models with existing process-based modeling results. Overall performance is comparable with the process-based approaches, but with significantly less modeling effort and resources.     

Using spatial multilevel regression analysis to assess soil type contextual effects on neural tube defects

The rate of neural tube defects (NTDs) in Shanxi Province is the highest world widely. Both human and environmental factors can induce NTDs, but various studies ignored contextual effects. This research examines whether there are significant soil type contextual effects on the rate of NTDs. A spatial two-level regression model is used to quantify the magnitude of contextual effects. Spatial autocorrelated errors structure is used to control autocorrelation of residuals. The results suggest that the spatial multilevel model fit the data better than non-spatial multilevel models. Our findings indicate that there are significant soil type contextual effects on the rate of NTDs, even after taking into account of fertilizer and net income. More attentions should be focused on how characteristics of each soil type may affect the rates of NTDs in further studies, which is a relevant issue for understanding etiology of NTDs.