Using Perl for Statistics: Data Processing and Statistical Computing

In this paper we show how Perl, an expressive and extensible high-level programming language, with network and ob ject-oriented programming support, can be used in processing data for statistics and statistical computing. The paper is organized in two parts. In Part I, we introduce the Perl programming language, with particular emphasis on the features that distinguish it from conventional languages. Then, using practical examples, we demonstrate how Perl's distinguishing features make it particularly well suited to perform labor intensive and sophisticated tasks ranging from the preparation of data to the writing of statistical reports. In Part II we show how Perl can be extended to perform statistical computations using modules and by "embedding" specialized statistical applications. We provide example on how Perl can be used to do simple statistical analyses, perform complex statistical computations involving matrix algebra and numerical optimization, and make statistical computations more easily reproducible. We also investigate the numerical and statistical reliability of various Perl statistical modules. Important computing issues such as ease of use, speed of calculation, and efficient memory usage, are also considered.

Integrated regulation of nonpoint pollution : combining managerial controls and economic instruments under multiple environmental targets

Regulators are often reluctant to rely solely on economic incentives to achieveenvironmental standards. We evaluate a "mixed approach" of economic instruments andmanagement standards when two environmental objectives need to be met simultaneously:minimum river flow rates and reductions in nitrate pollution. We show how the relativeefficiency of such mixed approaches can depend on exogenous factors, in this case weatherconditions. Results indicate that mixed instruments outperform stand-alone economicincentives or managerial controls under wet weather conditions, but not in 'average' years.However, the relative cost-effectiveness of mixed approaches increases considerably at higher levels of environmental standard compliance

Economic applications of nonparametric methods

This thesis deals with the subject of nonparametric methods, focusing on application to economic issues. Chapter 2 introduces the basic nonparametric methods underlying the applications in the subsequent chapters. In Chapter 3 we propose some basic standards to improve the use and reporting of nonparametric methods in the statistics and economics literature for the purpose of accuracy and reproducibility. We make recommendations on four aspects of the application of nonparametric methods: computational practice, published reporting, numerical accuracy, and visualization. In Chapter 4 we investigate the effect of life-cycle factors and other demographic characteristics on income inequality in the UK. Two conditional inequality measures are derived from estimating the cumulative distribution function of household income, conditional upon a broad set of explanatory variables. Estimation of the distribution is carried out using a semiparametric approach. The proposed inequality estimators are easily interpretable and are shown to be consistent. Our results indicate the importance of interfamily differences in the analysis of income distribution. In addition, our estimation procedure uncovers higher-order properties of the income distribution and non-linearities of its moments that cannot be captured by means of a "standard" parametric approach. Several features of the conditional distribution of income are highlighted. Chapter 5 we reexamine the relationship between openness to trade and the environment, controlling for economic development, in order to identify the presence of multiple regimes in the cross-country pollution-economic relationship. We first identify the presence of multiple regimes by using specification tests which entertain a single regime model as the null hypothesis. Then we develop an easily interpretable measure, based on an original application of the Blinder-Oaxaca decomposition, of the impact on the environment due to differences in regimes. Finally we apply a nonparametric recursive partitioning algorithm to endogenously identify various regimes. Our conclusions are threefold. First, we reject the null hypothesis that all countries obey a common linear model. Second, we find that quantitatively regime differences can have a significant impact. Thirdly, by using regression tree analysis we find subsets of countries which appear to possess very different environmental/economic relationships. In Chapter 6 investigate the existence of the so called environmental kuznets curve (EKC), the inverted-U shaped relationship between income and pollution, using nonparametric regression and a threshold regression methods. We find support for threshold models that lead to different reduced-form relationships between environmental quality and economic activity when early stages of economic growth are contrasted with later stages, There is no evidence of a common inverted U-shaped environment/economy relationship that all country follow as they grow. We also find that changes that might benefit the environment occur at much higher levels of income than those implied by standard models. Our findings support models in which improvements are a consequence of the deliberate introduction of policies addressing environmental concerns. Moreover, we find evidence that countries with low-income levels have a far greater variability in emissions per capita than high-income countries. This has the implication that it may be more difficult to predict emission levels for low-income countries approaching the turning point. A summary of the main findings and further research directions are presented in Chapter 7 and in Chapter 8, respectively

Carbon footprint of American lifestyles:A geodemographic segmentation approach

In order to deliver substantial reductions of U.S. residential emissions, cost-effective responses to climate change will need to recognize changes in consumer behavior and lifestyles as important mechanisms to mitigate carbon dioxide emissions. Marketing experts have long recognized the usefulness of developing composite variables to target specific consumer lifestyles and have subsequently developed market segmentation approaches to express relationships between geodemographics and consumer behavior. This paper represents the first use of detailed segmentation data to look at US footprint at high spatial resolution. We employ market segmentation data to delineate lifestyles for approximately 70 000 census tracts in the US and develop a spatial framework to better conceptualize lifestyles as location specific typologies of emission drivers. We find that lifestyles are not only very useful in explaining variations in emissions but in fact are as important as income, typically recognized as the major determinant of consumption emissions. Results from our analysis link the differences between suburban and urban footprints directly to lifestyle patterns and illustrate the geographic distribution of emissions resulting from households' consumption. We find that statistical clustering and consumer classification methods provide a unique perspective for understanding how various CO2 drivers interact and impact household emissions. Our proposed framework suggests that carbon mitigation strategies should move beyond a 'one-size-fits-all' approach centered on income and account for community specific lifestyle impacts related to consumer preferences and demographic characteristics at fine spatial scale

Boosting the eco-friendly sharing economy: The effect of gasoline prices on bikeshare ridership in three U.S. metropolises

Transportation has become the largest CO2 emitter in the United States in recent years with low gasoline prices standing out from many contributors. As demand side changes are called for reducing car use, the fast-growing sharing economy shows great potential to shift travel demand away from single-occupancy vehicles. Although previous inter-disciplinary research on shared mobility has explored its multitudes of benefits, it is yet to be investigated how the uptake of this eco-friendly sharing scheme is affected by gasoline prices. In this study, we examine the impact of gasoline prices on the use of bikeshare programs in three U.S. metropolises: New York City, Boston, and Chicago. Using bikeshare trip data, we estimate the impact of citywide gasoline prices on both bikeshare trip duration and trip frequency in a generalized linear regression setting. The results suggest that gasoline prices significantly affect bikeshare trip frequency and duration, with a noticeable surge in short trips. Doubling gasoline prices could help save an average of 1933 gallons of gasoline per day in the three cities, approximately 0.04% of the U.S. daily per capita gasoline consumption. Our findings indicate that fuel pricing could be an effective policy tool to support technology driven eco-friendly sharing mobility and boost sustainable transportation

Trasformazione tra datum e sistemi cartografici in ambito nazionale: implementazione di un software in ambiente GRASS e sue prestazioni

Selected paper presentato a GeoExplora Workshop 2004 - 6° Congresso MondoGIS Il lavoro illustra le caratteristiche e le prestazioni di un comando GRASS sviluppato per gestire le trasformazioni tra i sistemi geodetici (datum), (ed i corrispondenti cartografici) più utilizzati in ambito nazionale, Roma1940 (Gauss-Boaga), ED1950 (UTM-ED1950) e WGS84-ETRF89 (UTMWGS84- ETRF89) per scopi cartografici a scala medio-grande (1:5000)

Poverty eradication in a carbon constrained world

Partial funding for Open Access provided by the UMD Libraries' Open Access Publishing Fund.The UN Framework Convention on Climate Change aims to keep warming below 2 °C while recognizing developing countries’ right to eradicate extreme poverty. Poverty eradication is also the first of the Sustainable Development Goals. This paper investigates potential consequences for climate targets of achieving poverty eradication. We find that eradicating extreme poverty, i.e., moving people to an income above $1.9 purchasing power parity (PPP) a day, does not jeopardize the climate target even in the absence of climate policies and with current technologies. On the other hand, bringing everybody to a still modest expenditure level of at least$2.97 PPP would have long-term consequences on achieving emission targets. Compared to the reference mitigation pathway, eradicating extreme poverty increases the effort by 2.8% whereas bringing everybody to at least $2.97 PPP would increase the required mitigation rate by 27%. Given that the top 10% global income earners are responsible for 36% of the current carbon footprint of households; the discourse should address income distribution and the carbon intensity of lifestyles

Sample-based estimation of tree cover change in haiti using aerial photography:Substantial increase in tree cover between 2002 and 2010

Recent studies have used high resolution imagery to estimate tree cover and changes in natural forest cover in Haiti. However, there is still no rigorous quantification of tree cover change accounting for planted or managed trees, which are very important in Haiti’s farming systems. We estimated net tree cover change, gross loss, and gross gain in Haiti between 2002 and 2010 from a stratified random sample of 400 pixels with a systematic sub-sample of 25 points. Using 30 cm and 1 m resolution images, we classified land cover at each point, with any point touching a woody plant higher than 5 m classified as tree crown. We found a net increase in tree crown cover equiva-lent to 5.0 ± 2.3% (95% confidence interval) of Haiti’s land area. Gross gains and losses amounted to 9.0 ± 2.1% and 4.0 ± 1.3% of the territory, respectively. These results challenge, for the first time with empirical evidence, the predominant narrative that portrays Haiti as experiencing ongoing forest or tree cover loss. The net gain in tree cover quantified here represents a 35% increase from 2002 to 2010. Further research is needed to determine the drivers of this substantial net gain in tree cover at the national scale

Social interaction and geographic diffusion of iron‐biofortified beans in Rwanda

Abstract: This study analyzes smallholder farmers’ decisions to adopt beans with higher levels of dietary iron developed through a conventional breeding technique called biofortification. We approach this study by applying spatial econometric techniques to estimate neighborhood influence and to determine the factors driving the adoption of iron‐biofortified beans (IBB). We employ a cross‐sectional, nationally representative survey of bean producing households from 2015 bean growing season B in Rwanda, and present results for growers of both bush and climbing varieties of beans. The results show geographic diffusion of iron bean planting material occurs among neighboring farmers that exhibit interdependent decision‐making patterns, as well as similar characteristics relative to the group. Some policy implications can be drawn from the results. First, a differentiated geographical targeting strategy for bush and climbing bean varieties as a function of farmer and farm characteristics should increase iron bean adoption rates. Second, strengthening partnerships with delivery agents and extensionists should stimulate the adoption of IBB varieties. And finally, technology‐promotion programs that consider progressive farmers and strengthen social interactions and group activities among peer networks should increase the spread of information and diffusion of IBB

Carbon footprints of cities and other human settlements in the UK

A growing body of literature discusses the CO2 emissions of cities. Still, little is known about emission patterns across density gradients from remote rural places to highly urbanized areas, the drivers behind those emission patterns and the global emissions triggered by consumption in human settlements—referred to here as the carbon footprint. In this letter we use a hybrid method for estimating the carbon footprints of cities and other human settlements in the UK explicitly linking global supply chains to local consumption activities and associated lifestyles. This analysis comprises all areas in the UK, whether rural or urban. We compare our consumption-based results with extended territorial CO2 emission estimates and analyse the driving forces that determine the carbon footprint of human settlements in the UK. Our results show that 90% of the human settlements in the UK are net importers of CO2 emissions. Consumption-based CO2 emissions are much more homogeneous than extended territorial emissions. Both the highest and lowest carbon footprints can be found in urban areas, but the carbon footprint is consistently higher relative to extended territorial CO2 emissions in urban as opposed to rural settlement types. The impact of high or low density living remains limited; instead, carbon footprints can be comparatively high or low across density gradients depending on the location-specific socio-demographic, infrastructural and geographic characteristics of the area under consideration. We show that the carbon footprint of cities and other human settlements in the UK is mainly determined by socio-economic rather than geographic and infrastructural drivers at the spatial aggregation of our analysis. It increases with growing income, education and car ownership as well as decreasing household size. Income is not more important than most other socio-economic determinants of the carbon footprint. Possibly, the relationship between lifestyles and infrastructure only impacts carbon footprints significantly at higher spatial granularity