Do natural resources and economic components exhibit differential quantile environmental effects?

In view of the resource curse assumption, the environmental aspects of resource utilization are arguably posing more dangers to human existence. In the Middle East and North Africa (MENA) region, the region that holds more than 60% and 50% of the world's oil and gas reserves respectively, the need to examine the contribution of natural resources to environmental quality among other factors cannot be overemphasized. By leveraging on the novelty of observing the differential impact of natural resources and other economic components such as income and primary energy utilizations across the quantiles of carbon emission, this study implements the quantile regression approach alongside other relevant techniques to analyze data between 1990 and 2018 for selected countries in the MENA region including Saudi Arabia, Iran, Kuwait, Qatar, Algeria, Morocco, Oman, Egypt, and the United Arab Emirates (UAE). The result posits that natural resource utilization generally hampers the environment across the quantiles. However, this negative effect decreases until the 50th quantile before starting to rise again toward the upper quantiles. Additionally, primary energy utilization and globalization respectively worsen and improve environmental quantile, especially toward the upper quantiles while income affirms the inverted U-shaped hypothesis across the entire quantiles. Moreover, there is a statistically significant one-way directional causality from natural resources, economic expansion, primary energy use, and globalization to carbon emission levels. Hence, the study offers environmentally friendly resource utilization policies to the MENA economies and other resource-rich states by extension.     

Bayesian Nonstationary Frequency Analysis of Hydrological Variables1

Ouarda, T.B.M.J. and S. El‐Adlouni, 2011. Bayesian Nonstationary Frequency Analysis of Hydrological Variables. Journal of the American Water Resources Association (JAWRA) 47(3):496‐505. DOI: 10.1111/j.1752‐1688.2011.00544.x Abstract: The present paper provides a discussion of nonstationary frequency analysis models in hydrology with a focus on the Bayesian approach. The Bayesian model provides an efficient estimation framework of hydrological quantiles in the presence of nonstationarity. In nonstationary frequency analysis models, the parameters are functions of covariates, allowing for dependent parameters and trends. The use of the nonstationary Generalized Maximum Likelihood Estimation method in hydrologic frequency analysis is discussed. This model allows using prior information concerning the variables under study and considering a number of models (linear, quadratic, etc.) of the dependence of the parameters on covariates. A discussion is also provided concerning the use of the reversible jump Monte Carlo Markov Chain procedure which allows carrying out the estimation of the posterior distributions of the parameters and the selection of the Bayesian model at the same time. An application to a case study is presented to illustrate the potential of the model.     

CATCHMENT RESPONSE TIME AS A PREDICTOR OF FLOOD QUANTILES1

ABSTRACT: A regression analysis using a generalized least squares approach on flow data from the driftless area of Wisconsin indicates that the ratio of drainage area to time-to-peak is a good predictor of flood quantiles. The estimation of time-to-peak (or some other measure of basin response time) requires direct measurement of river stage and possibly rainfall at the site of which the quantiles are to be estimated. The cost-effectiveness of such an approach must yet be determined.     

Assessment Tools for Urban Catchments: Developing Biological Indicators Based on Benthic Macroinvertebrates1

Abstract: Biological indicators, particularly benthic macroinvertebrates, are widely used and effective measures of the impact of urbanization on stream ecosystems. A multimetric biological index of urbanization was developed using a large benthic macroinvertebrate dataset (n = 1,835) from the Baltimore, Maryland, metropolitan area and then validated with datasets from Cleveland, Ohio (n = 79); San Jose, California (n = 85); and a different subset of the Baltimore data (n = 85). The biological metrics used to develop the multimetric index were selected using several criteria and were required to represent ecological attributes of macroinvertebrate assemblages including taxonomic composition and richness (number of taxa in the insect orders of Ephemeroptera, Plecoptera, and Trichoptera), functional feeding group (number of taxa designated as filterers), and habit (percent of individuals which cling to the substrate). Quantile regression was used to select metrics and characterize the relationship between the final biological index and an urban gradient (composed of population density, road density, and urban land use). Although more complex biological indices exist, this simplified multimetric index showed a consistent relationship between biological indicators and urban conditions (as measured by quantile regression) in three climatic regions of the United States and can serve as an assessment tool for environmental managers to prioritize urban stream sites for restoration and protection.     

Daily Precipitation Extremes in Iran: Decadal Anomalies and Possible Drivers

This study focuses on the empirical statistical analysis of the anomalies in daily precipitation extremes by applying the quantile perturbation method (QPM) to data from 31 Iranian weather stations during the period between 1961 and 2005. The possible causes behind the anomalies in precipitation extremes are identified by analyzing their relationship with the anomalies in eight atmospheric indices (i.e., NAO, SOI, PDO, AMO, NCP, DMI, WeMO, SSN). In terms of decadal oscillations, the country was generally wet in the 1960s and 1970s with most stations exhibiting periods of higher quantile perturbations, whereas lower quantile perturbations were dominant in the 1980s and 1990s. The highest perturbation in extreme precipitation quantiles prevails in Central Iran during the early 1980s, in which the quantiles are about 50% higher than the ones based on the full time series. The frequency of significant precipitation anomalies for winter season was greater than that for spring and autumn seasons. For the summer season, the humid region in North Iran demonstrates strong positive anomalies. The results highlight the noticeable role of large‐scale climatic factors in the anomalous behavior of precipitation extremes in Iran. The atmospheric drivers of the quantile anomalies in extreme precipitation were found to differ from one season to another.     

The Influence of the Pacific Decadal Oscillation on Annual Floods in the Rivers of Western Canada

We analyzed annual peak flow series from 127 naturally flowing or naturalized streamflow gauges across western Canada to examine the impact of the Pacific Decadal Oscillation (PDO) on annual flood risk, which has been previously unexamined in detail. Using Spearman's rank correlation ρ and permutation tests on quantile‐quantile plots, we show that higher magnitude floods are more likely during the negative phase of the PDO than during the positive phase (shown at 38% of the stations by Spearman's rank correlations and at 51% of the stations according to the permutation tests). Flood frequency analysis (FFA) stratified according to PDO phase suggests that higher magnitude floods may also occur more frequently during the negative PDO phase than during the positive phase. Our results hold throughout much of this region, with the upper Fraser River Basin, the Columbia River Basin, and the North Saskatchewan River Basin particularly subject to this effect. Our results add to other researchers' work questioning the wholesale validity of the key assumption in FFA that the annual peak flow series at a site is independently and identically distributed. Hence, knowledge of large‐scale climate state should be considered prior to the design and construction of infrastructure.     

基于MGWR的土壤pH值空间建模及其影响因素分析

以安徽、河南、江苏和山东省为研究区,利用599个土壤样点数据,从地形、气候和生物等方面选取与土壤pH相关的9个环境因子,采用多尺度地理加权回归（MGWR）、混合地理加权回归（Mixed GWR）、地理加权回归（GWR）和多元线性回归（MLR）这4种模型对研究区土壤pH空间分布进行建模,并结合MGWR与分位数回归揭示环境因子对土壤pH作用的空间差异性.结果表明：①研究区土壤pH在不同空间距离上呈不同程度的显著全局和局部空间自相关性,聚集特征明显.② 4种模型中MGWR模型最优,MGWR、Mixed GWR、GWR和MLR的建模集R_adj²为0.64、0.62、0.59和0.48.MGWR的残差独立分布性最强,其空间自相关性最弱,全局Moran''s I仅为0.07.③ 3种GWR预测结果显示,研究区土壤pH值空间分布总体由北至南逐渐降低,河南北部最高,安徽南部最低.④ MGWR回归结果表明年均降雨量（MAP）、多尺度谷底平坦度（MRVBF）和海拔对土壤pH的影响较强,且存在较强的空间异质性.在江苏北部和山东大部分地区,MAP对土壤pH的影响较强;在江苏北部和山东西部,MRVBF对土壤pH的正向作用较强;在江苏北部和中部,海拔对土壤pH的负向作用最强.⑤ MAP对不同分位数水平上的土壤pH均呈显著负作用,作用强度随分位数水平增加呈减弱趋势;MRVBF对低分位数水平（θ为0.1~0.4）上的土壤pH呈显著负作用,对高分位数水平（θ为0.5~0.9）的土壤pH作用不显著.研究结果可为利用MGWR开展大区域土壤属性影响因素分析及预测制图提供参考.     

Panel heterogeneous distribution analysis of trade and modernized agriculture on CO2 emissions: The role of renewable and fossil fuel energy consumption

In line with the global target of reducing climate change and its impact, this study explored the causal relationship between CO₂ emissions, modernized agriculture, trade openness, aggregate and disaggregate energy consumption in 14 African countries from 1990–2013 using a panel quantile estimation procedure. The empirical results showed that value addition to agricultural commodities declines CO₂ emissions in countries with high pollution levels. The study revealed a positive nexus between CO₂ emissions and energy consumption homogeneously distributed across quantiles. Trade openness was found to lower CO₂ emissions in countries with lower and higher levels of environmental pollution. While fossil fuel energy consumption was found to exacerbate CO₂ emissions, renewable energy consumption confirmed its mitigating effect on environmental pollution. The institution of climate‐smart agricultural options will sustainably increase productivity and income while adapting to climate change by reducing greenhouse gas emissions. Diversification of energy technologies with clean and modern energy sources like renewables avoid the over‐dependence on fossil fuels for agricultural purposes. Trade policies can stimulate flows of technology and investment opportunities for specialization in production and economies of scale. Hence, the consideration of policies that boost agricultural sector productivity and create an efficient market for international trade in Africa will help in improving livelihoods.     

基于环境承载力的京津冀雾霾治理政策效果评估

雾霾污染治理是京津冀协同发展需要解决的重大问题。2013年9月颁布的"大气污染防治行动计划(大气国十条)"明确提出了京津冀地区雾霾治理目标,各地区也制定了雾霾污染治理的政策措施。本文旨在环境承载力分析的基础上评估雾霾治理的政策效果。首先,分析了京津冀地区大气环境污染特征,并结合相关文献确定京津冀地区雾霾治理的主要影响因素为污染物排放、风力以及相邻地区的传输效应等;其次,将影响PM_(2.5)浓度主要因素进行统计建模,并采用分位数回归模型进行矫正,大大提高模型的拟合精度;再次,基于大气国十条规定的京津冀各地区的PM_(2.5)年均浓度目标计算各地区的大气环境承载力;最后,在假定风力等气象条件不变的情况下,根据大气国十条规定的京津冀地区的污染物排放量利用统计模型模拟2017年的雾霾污染水平,模拟除张家口、承德和秦皇岛以外其余10个地区年均浓度60μg/m~3和70μg/m~3目标下PM_(2.5)日均浓度发生频率的变化情况,评估和讨论大气国十条提出的京津冀雾霾治理目标。结果表明:按照大气国十条减排计划的京津冀地区污染物排放量普遍高于其PM_(2.5)浓度目标下的大气环境容量(邯郸市除外),即大气国十条所规定的减排措施难以实现既定的PM_(2.5)浓度目标;PM_(2.5)年均浓度目标从60μg/m~3上升到70μg/m~3,重污染天气发生频率上升有限,大气污染物的减排量却显著下降。因此,要实现既定的雾霾浓度控制目标,天津和河北需要进一步加大污染物减排力度;雾霾治理应注重减少重污染天气的发生频率,治理重点应转向重度雾霾发生频率较高的冬季污染物排放控制;在科学确定环境承载力的基础上,确定切实可行的PM_(2.5)浓度控制目标,制定具有可操作性的污染物减排计划。     

Heterogeneous effects of urbanization and environment Kuznets curve hypothesis in Africa

This work explores the heterogeneous effect of urbanization and nonrenewable energy consumption on the environment in 54 African nations. Panel data were used from 1996 to 2019. For estimation, panel quantile regression analysis, augmented mean group, panel threshold regression, and the environment Kuznets curve hypothesis were applied to check the relationship between income and carbon emissions. The study's outcome demonstrates that urbanization and nonrenewable energy consumption degrade the environment in Africa. Furthermore, an inverted U-shape relationship exists between economic growth and CO₂ emissions, confirming the environmental Kuznets curve hypothesis. The findings indicate that urbanization should be planned; otherwise, urbanization can cause environmental degradation. African countries must adopt green urbanization and use renewable energy and clean manufacturing technologies. The institutions are encouraged to execute the standard, regulatory environment, and policies to reduce carbon emissions. Countries throughout the African continent should actively respond to the issues by charting a separate and diverse route for urban development.