52,771 research outputs found
Have Countries with Lax Environmental Regulations a Comparative Advantage in Polluting Industries?
We aim to study whether lax environmental regulations induce comparative advantages, causing the least-regulated countries to specialize in polluting industries. The study is based on Trefler and Zhuâs (2005) definition of the factor content of trade. For the econometrical analysis, we use a cross-section of 71 countries in 2000 to examine the net exports in the most polluting industries. We try to overcome three weaknesses in the empirical literature: the measurement of environmental endowments or environmental stringency, the possible endogeneity of the explanatory variables, and the influence of the industrial level of aggregation. As a result, we do find some evidence in favor of the pollution-haven effect. The exogeneity of the environmental endowments was rejected in several industries, and we also find that industrial aggregation matters.comparative advantage, environmental regulation, trade, pollution haven, Porter hypothesis
How inefficient are small-scale rice farmers in eastern India really?: Examining the effects of microtopography on technical efficiency estimates
We focus on the impact of failing to control for differences in land types defined along toposequence on estimates of farm technical efficiency for small-scale rice farms in eastern India. In contrast with the existing literature, we find that those farms may be considerably more technically efficient than they appear from more aggregated analysis without such control. Farms planted with modern rice varieties are technically efficient. Furthermore, farms planted with traditional rice varieties operate close to the production frontier on less productive lands (upland and mid-upland), but significant technical inefficiency exists on more productive lands (medium land and lowland).
A Review of the Enviro-Net Project
Ecosystems monitoring is essential to properly understand their development
and the effects of events, both climatological and anthropological in nature.
The amount of data used in these assessments is increasing at very high rates.
This is due to increasing availability of sensing systems and the development
of new techniques to analyze sensor data. The Enviro-Net Project encompasses
several of such sensor system deployments across five countries in the
Americas. These deployments use a few different ground-based sensor systems,
installed at different heights monitoring the conditions in tropical dry
forests over long periods of time. This paper presents our experience in
deploying and maintaining these systems, retrieving and pre-processing the
data, and describes the Web portal developed to help with data management,
visualization and analysis.Comment: v2: 29 pages, 5 figures, reflects changes addressing reviewers'
comments v1: 38 pages, 8 figure
Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes
Time series forecasting is an important predictive methodology which can be
applied to a wide range of problems. Particularly, forecasting the indoor
temperature permits an improved utilization of the HVAC (Heating, Ventilating
and Air Conditioning) systems in a home and thus a better energy efficiency.
With such purpose the paper describes how to implement an Artificial Neural
Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous
intelligent wireless sensor network. The present paper uses a Wireless Sensor
Networks (WSN) to monitor and forecast the indoor temperature in a smart home,
based on low resources and cost microcontroller technology as the 8051MCU. An
on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs,
has been developed for real-time time series learning. It performs the model
training with every new data that arrive to the system, without saving enormous
quantities of data to create a historical database as usual, i.e., without
previous knowledge. Consequently to validate the approach a simulation study
through a Bayesian baseline model have been tested in order to compare with a
database of a real application aiming to see the performance and accuracy. The
core of the paper is a new algorithm, based on the BP one, which has been
described in detail, and the challenge was how to implement a computational
demanding algorithm in a simple architecture with very few hardware resources.Comment: 28 pages, Published 21 April 2015 at MDPI's journal "Sensors
Econometrics meets sentiment : an overview of methodology and applications
The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software
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Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California
This interdisciplinary eXtensible Building Operating SystemâVehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits
Temporal and spatial homogeneity in air pollutants panel EKC estimations: Two nonparametric tests applied to Spanish provinces
Although panel data have been used intensively by a wealth of studies investigating the GDP-pollution relationship, the poolability assumption used to model these data is almost never addressed. This paper applies a strategy to test the poolability assumption with methods robust to functional misspecification. Nonparametric poolability tests are performed to check the temporal and spatial homogeneity of the panel and their results are compared with the conventional F-tests for a balanced panel of 48 Spanish provinces on four air pollutant emissions (CH4, CO, CO2 and NMVOC) over the 1990-2002 period. We show that temporal homogeneity may allow the pooling of the data and drive to well-defined nonparametric and parametric cross-sectional U-inverted shapes for all air pollutants. However, the presence of spatial heterogeneity makes this shape compatible with different timeseries patterns in every province - mainly increasing or decreasing depending on the pollutant. These results highlight the extreme sensitivity of the income-pollution relationship to region- or country-specific factors.Environmental Kuznets Curve; Air pollutants; Non/Semiparametric estimations; Poolability tests
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