Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations

We introduce robust procedures for analyzing water quality data collected over time. One challenging task in analyzing such data is how to achieve robustness in presence of outliers while maintaining high estimation efficiency so that we can draw valid conclusions and provide useful advices in water management. The robust approach requires specification of a loss function such as the Huber, Tukey’s bisquare and the exponential loss function, and an associated tuning parameter determining the extent of robustness needed. High robustness is at the cost of efficiency loss in parameter loss. To this end, we propose a data-driven method which leads to more efficient parameter estimation. This data-dependent approach allows us to choose a regularization (tuning) parameter that depends on the proportion of “outliers” in the data so that estimation efficiency is maximized. We illustrate the proposed methods using a study on ammonium nitrogen concentrations from two sites in the Huaihe River in China, where the interest is in quantifying the trend in the most recent years while accounting for possible temporal correlations and “irregular” observations in earlier years.     

The Impact of Succession Stage on Share and Traits of Clonal Plants in Abandoned <Emphasis Type="Italic">Molinion caeruleae</Emphasis> Meadows

The impact of succession stage on the share of clonal plants in the plant cover, seed rain and seedling pool has been studied in the years 2014 and 2015. The investigations were carried out in abandoned Molinion caeruleae meadows dominated by small-statured species (Patch I), tall growing large-tussock grasses (Patch II) and shrub-willows (Patch III). Although the clonal taxa dominated in the plant cover, seed rain and seedling pool, their share in subsequent study sites was quite different. The spectra of life-history traits relevant to clonality in the plant cover and seed rain were similar and did not vary significantly among subsequent locations, while they mostly differed as observed in the seedling pool. The various spectra observed in seedling pool in subsequent study sites might be due to fact that the seed germination and recruitment of offspring are strongly influenced by habitat conditions which varied remarkably in the successive Patches.     

Periphyton Developed on Artificial Substrates: Effect of Substrate Type and Incubation Depth

The aim of this study was to assess the effect of substrate type and incubation depth on periphyton that had developed on artificial substrates. Uniform rectangular tiles made out of artificial substrates: glass, ceramic, willow tree and yew tree, were fixed on a floating buoy and deployed at three different depths in a photic zone of the Sava Lake (Belgrade, Serbia). Non-taxonomic attributes in the developed biofilm were estimated week-by-week from the start of the experiment in July, until its end in September 2014. Through assessment of substrate type and depth of incubation effect we concluded that these parameters for the fact influence periphyton development and composition. Glass was preferred by autotrophic component over ceramic and wooden substrates. In general, substrate type effect was diminished by increasing incubation depth. When non-taxonomic parameters are to be used in biomonitoring studies, our results suggest that glass substrate and shallow layer of water column (up to 50 cm) for incubation should be preferred.     

Investigating the oil curse in OECD and Non-OECD oil-exporting economies using green measures of income

Empirical studies investigating the natural resource curse theory mostly employ cross-country and panel regression techniques subject to endogeneity bias. Also, most of these studies employ GDP in its aggregate or per-capita terms as the outcome variable in their analyses. However, the use of GDP measures of income for resource curse investigations may not portray the true incomes of resource-intensive economies. Standard national accounts treat natural resource rents as a positive contribution to income without adjusting for the value of depleted natural resource stock. This treatment often leads to a positive bias in the national income computations of resource-rich economies. Unlike previous studies, we test the robustness of the curse in the predominantly used measures of national income, GDP, by investigating the theme in genuine income measures of economic output as well. Also, the paper deviates from most empirical studies in the literature by using the Arellano–Bond difference GMM method in investigating the oil curse in OECD and Non-OECD oil-exporting economies. Additionally, we employ two alternative measures of resource intensity in our explorations; the share of oil rents in GDP and per-capita oil reserves. Our results provide evidence of the curse in Non-OECD countries employing aggregate and per-capita measures of genuine income.     

Sustainability indicators in the swine industry of the Brazilian State of Santa Catarina

In recent decades, there has been a growing concern about measuring and evaluating the results of intensive production practices, such as the mode of integration practiced on a large scale by agro-industries. The present study discusses the application and the results of a Sustainability Indicators System in the context of the swine industry in the Brazilian State of Santa Catarina. These indicators, focused on the level of sustainability maturity, can be used as a way to diagnose and compare the efficiency of the industry from a broader perspective. The research findings indicate that most of the pig farms that have been studied can be classified as “in search of sustainability or sustainable.” Nevertheless, there are differences among the dimensions and between the most ranked farms and the others which must be considered for a better design and application of public policies and company strategies toward an effective and balanced social, economic and environmental sustainability.     

Discerning the factors explaining the change in energy efficiency

Due to the high rates of energy consumption and its impact on environment over the last decades, policy decision-makers are increasingly recognising the need to take actions that allow to address problems associated with the deployment of non-renewable resources and climate changes. One field of action has been the promotion of measures that contribute to improve energy efficiency of countries. The purpose of this study is to identify the main factors explaining changes in energy efficiency applying the multiplicative Log Mean Divisia Index decomposition method for a set of countries (Portugal, UK, Brazil and China) with different socio-economic background and energy mix. The results show that overall energy efficiency trends display different patterns between countries and the same happens within each country from a sectoral perspective. Major drivers of improvements of overall energy efficiency were the intensity effect and the affluence effect, whereas the driver that hampered those improvements was the energy consumption per capita. Some policy implications derived from the results achieved are: policy decision-makers should support measures that promote the adoption of energy-saving technologies resulting from new technological developments; policy measures should be directed to raise awareness of end-users regarding energy efficiency and energy conservation efforts; policy measures promoting economic growth through the development (or expansion) of sectors of activity that consume less energy can also be implemented; finally, policy instruments may also be used to reduce the costs of implementing energy efficiency and energy-saving measures to households and firms.     

Discrete-choice experiments valuing local environmental impacts of renewables: two approaches to a case study in Portugal

Despite the often mentioned environmental benefits associated with transition from fossil fuels to renewable energy sources, their use for electricity production has non-negligible negative environmental impacts. The most commonly mentioned in surveys concern different types of landscape impacts, impacts on the fauna and flora, and noise. These impacts differ by size and location of plants, and by source of energy, rendering the policy decision complex. In addition, there are other welfare issues to take into consideration, as positive and negative environmental impacts are not evenly distributed among population groups. This paper proposes to compare the welfare impacts of renewable energy sources controlling for the type of renewable as well as the specific environmental impact by source. To this end, two discrete-choice experiments are designed and applied to a national sample of the Portuguese population. In one case, only individual negative impacts of renewables are used, and in another case, the negative impacts interact with a specific source. Results show the robustness of discrete-choice experiments as a method to estimate the welfare change induced by the impacts of renewable energy sources. Overall, respondents are willing to pay to reduce the environmental impacts, thus making compensation for local impacts feasible. Moreover, the estimations reveal that respondents are significantly sensitive to the detrimental environmental effects of specific renewable energy sources, being willing to pay more to use these sources of energy relative to others.     

Pollutant versus non-pollutant generation technologies: a CML-analogous analysis

In this work, we apply the Modern Portfolio Theory and the Capital Assets Pricing Model financial tools to a portfolio of CO₂-emitting generation technologies under diverse scenarios. We will calculate the efficient—in the sense of having the minimum risk for a given level of emissions—portfolios frontier. The Capital Market Line (CML) is the place where all the possible combinations of a specific efficient portfolio and a pollution-free portfolio—made up with nuclear and renewable generation technologies—lie. In Finance, that specific efficient portfolio is called the market portfolio but we will see that in our case it lacks an evident meaning. Therefore, we will explain which should be the reference portfolio for power generation planning analysis. Anyway, the fact is that those combinations are less pollutant than the portfolios in the efficient frontier. Thus, a policy-maker can analyse which is their effect on emissions reduction. We will start analysing the efficient pollutant generation portfolios. Then, we will introduce the CML-analogous lines (CML-A) to allow the possibility of reducing emissions by combining an efficient portfolio with a non-pollutant portfolio—this non-pollutant portfolio is free of both emissions and risk. Results support the necessity of considering the carbon capture and storage technology to achieve a less risky generation mix, with less emissions and allowing a higher diversification due to the presence of cleaner fossil fuel technologies. All of that leads to better levels of energy security.     

Model-based analysis of urban settlement process in eco-sensitive area of developing country: a study with special reference to hills of an Indian city

Rapid and unplanned expansion of a city into its eco-sensitive areas like hills, wetlands, and forests is becoming a major concern, particularly in developing countries. Understanding the process and causes of such unplanned urban expansion is of paramount importance for framing sustainable urban development policies. This paper presents a modelling concept that relates urban settlement in such eco-sensitive areas with potential socio-economic, demographic and geographical factors. The model is applied to an Indian city Guwahati, which is experiencing serious environmental degradation due to unplanned urban settlement in its eco-sensitive hilly areas. While topographical and settlement data were derived using satellite data in GIS platform, all other necessary data were collected from relevant government organizations. On validation of the multi-linear regression model, the coefficient of determination and the root mean square error are obtained as 0.938 and 1.7, respectively. Model results show that geographical parameters are less influencing as compared to the other socio-economic and demographic factors. Sensitivity analysis of urban settlements in hills of Guwahati city carried out with respect to the considered factors reveals that land value and free space availability in the surrounding area of a hill are the most sensitive parameters. This indicates that city development plans should give more importance to outward spatial expansion in plain areas with regulated land value and zoning scheme to minimize unauthorized settlement in eco-sensitive hilly areas of Guwahati city.