Carbon dioxide emissions and international trade at the turn of the millennium

We present a new dataset of geographical production-, final (embodied) production-, and consumption-based carbon dioxide emission inventories, covering 78 regions and 55 sectors from 1997 to 2011. We extend previous work both in terms of time span and in bridging from geographical to embodied production and, ultimately, to consumption. We analyse the recent evolution of emissions, the development of carbon efficiency of the global economy, and the role of international trade. As the distribution of responsibility for emissions across countries is key to the adoption and implementation of international environmental agreements and regulations, the final production- and consumption-based inventories developed here provide a valuable extension to more traditional geographical production-based criteria.

Trade, economic growth and environment

The literature on trade openness, economic development, and the environment is largely inconclusive about the environmental consequences of trade. This study review previous studies focusing on treating trade and income as endogenous and estimating the overall impact of trade openness on environmental quality using the instrumental variables technique. The results show that whether or not trade has a beneficial effect on the environment varies depending on the pollutant and the country. Trade is found to benefit the environment in OECD countries. It has detrimental effects, however, on sulfur dioxide (SO2) and carbon dioxide (CO2) emissions in non-OECD countries, although it does lower biochemical oxygen demand (BOD) emissions in these countries. The results also find the impact is large in the long term, after the dynamic adjustment process, although it is small in the short term.

China's inter-regional spillover of carbon emissions and domestic supply chains

In this study, we apply the inter-regional input–output model to explain the relationship between China’s inter-regional spillover of CO2 emissions and domestic supply chains for 2002 and 2007. Based on this model, we propose alternative indicators such as the trade in CO2 emissions, CO2 emissions in trade, regional trade balances, and comparative advantage of CO2 emissions. The empirical results not only reveal the nature and significance of inter-regional environmental spillover within China’s domestic regions but also demonstrate how CO2 emissions are created and distributed across regions via domestic production networks. The main finding shows that a region’s CO2 emissions depend on not only its intra-regional production technique, energy use efficiency but also its position and participation degree in domestic and global supply chains.

Tracing CO2 emissions in global value chains

This paper integrates two lines of research into a unified conceptual framework: trade in global value chains and embodied emissions. This allows both value added and emissions to be systematically traced at the country, sector, and bilateral levels through various production network routes. By combining value-added and emissions accounting in a consistent way, the potential environmental cost (amount of emissions per unit of value added) along global value chains can be estimated. Using this unified accounting method, we trace CO2 emissions in the global production and trade network among 41 economies in 35 sectors from 1995 to 2009, basing our calculations on the World Input–Output Database, and show how they help us to better understand the impact of cross-country production sharing on the environment.

Production sharing, demand spillovers and CO2 emissions : the case of Chinese regions in GVCs

This study adopts the perspective of demand spillovers to provide new insights regarding Chinese domestic-regions' production position in global value chains and their associated CO2 emissions. To this end, we constructed a new type of World Input-Output Database in which China's domestic interregional input-output table for 2007 is endogenously embedded. Then, the pattern of China's regional demand spillovers across both domestic regions and countries are revealed by employing this new database. These results were further connected to endowments theory, which help to make sense of the empirical results. It is found that China's regions locate relatively upstream in GVCs, and had CO2 emissions in net exports, which were entirely predicted by the environmental extended HOV model. Our study points to micro policy instruments to combat climate change, for example, the tax reform for energy inputs that helps to change the production pattern thus has impact on trade pattern and so forth.

A unified framework of trade in value added : physical, monetary, exchange rates, and GHG emissions

Koopman et al. (2014) developed a method to consistently decompose gross exports in value-added terms that accommodate infinite repercussions of international and inter-sector transactions. This provides a better understanding of trade in value added in global value chains than does the conventional gross exports method, which is affected by double-counting problems. However, the new framework is based on monetary input--output (IO) tables and cannot distinguish prices from quantities; thus, it is unable to consider financial adjustments through the exchange market. In this paper, we propose a framework based on a physical IO system, characterized by its linear programming equivalent that can clarify the various complexities relevant to the existing indicators and is proved to be consistent with Koopman's results when the physical decompositions are evaluated in monetary terms. While international monetary tables are typically described in current U.S. dollars, the physical framework can elucidate the impact of price adjustments through the exchange market. An iterative procedure to calculate the exchange rates is proposed, and we also show that the physical framework is also convenient for considering indicators associated with greenhouse gas (GHG) emissions.

How does firm heterogeneity information impact the estimation of embodied carbon emissions in Chinese exports?

Using an augmented Chinese input–output table in which information about firm ownership and type of traded goods are explicitly reported, we show that ignoring firm heterogeneity causes embodied CO2 emissions in Chinese exports to be overestimated by 20% at the national level, with huge differences at the sector level, for 2007. This is because different types of firm that are allocated to the same sector of the conventional Chinese input–output table vary greatly in terms of market share, production technology and carbon intensity. This overestimation of export-related carbon emissions would be even higher if it were not for the fact that 80% of CO2 emissions embodied in exports of foreign-owned firms are, in fact, emitted by Chinese-owned firms upstream of the supply chain. The main reason is that the largest CO2 emitter, the electricity sector located upstream in Chinese domestic supply chains, is strongly dominated by Chinese-owned firms with very high carbon intensity.

Consumption-based accounting of U.S. CO2 emissions from 1990 to 2010

To tackle global climate change, it is desirable to reduce CO2 emissions associated with household consumption in particular in developed countries, which tend to have much higher per capita household carbon footprints than less developed countries. Our results show that carbon intensity of different consumption categories in the U.S. varies significantly. The carbon footprint tends to increase with increasing income but at a decreasing rate due to additional income being spent on less carbon intensive consumption items. This general tendency is frequently compensated by higher frequency of international trips and higher housing related carbon emissions (larger houses and more space for consumption items). Our results also show that more than 30% of CO2 emissions associated with household consumption in the U.S. occur outside of the U.S. Given these facts, the design of carbon mitigation policies should take changing household consumption patterns and international trade into account.

Aplicación de técnicas de inteligencia artificial a la predicción de contaminantes atmosféricos

Abstract Air pollution is a big threat and a phenomenon that has a specific impact on human health, in addition, changes that occur in the chemical composition of the atmosphere can change the weather and cause acid rain or ozone destruction. Those are phenomena of global importance. The World Health Organization (WHO) considerates air pollution as one of the most important global priorities. Salamanca, Gto., Mexico has been ranked as one of the most polluted cities in this country. The industry of the area led to a major economic development and rapid population growth in the second half of the twentieth century. The impact in the air quality is important and significant efforts have been made to measure the concentrations of pollutants. The main pollution sources are locally based plants in the chemical and power generation sectors. The registered concerning pollutants are Sulphur Dioxide (SO2) and particles on the order of ∼10 micrometers or less (PM10). The prediction in the concentration of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. In this PhD thesis we propose a model to predict concentrations of pollutants SO2 and PM10 for each monitoring booth in the Atmospheric Monitoring Network Salamanca (REDMAS - for its spanish acronym). The proposed models consider the use of meteorological variables as factors influencing the concentration of pollutants. The information used along this work is the current real data from REDMAS. In the proposed model, Artificial Neural Networks (ANN) combined with clustering algorithms are used. The type of ANN used is the Multilayer Perceptron with a hidden layer, using separate structures for the prediction of each pollutant. The meteorological variables used for prediction were: Wind Direction (WD), wind speed (WS), Temperature (T) and relative humidity (RH). Clustering algorithms, K-means and Fuzzy C-means, are used to find relationships between air pollutants and weather variables under consideration, which are added as input of the RNA. Those relationships provide information to the ANN in order to obtain the prediction of the pollutants. The results of the model proposed in this work are compared with the results of a multivariate linear regression and multilayer perceptron neural network. The evaluation of the prediction is calculated with the mean absolute error, the root mean square error, the correlation coefficient and the index of agreement. The results show the importance of meteorological variables in the prediction of the concentration of the pollutants SO2 and PM10 in the city of Salamanca, Gto., Mexico. The results show that the proposed model perform better than multivariate linear regression and multilayer perceptron neural network. The models implemented for each monitoring booth have the ability to make predictions of air quality that can be used in a system of real-time forecasting and human health impact analysis. Among the main results of the development of this thesis we can cite: A model based on artificial neural network combined with clustering algorithms for prediction with a hour ahead of the concentration of each pollutant (SO2 and PM10) is proposed. A different model was designed for each pollutant and for each of the three monitoring booths of the REDMAS. A model to predict the average of pollutant concentration in the next 24 hours of pollutants SO2 and PM10 is proposed, based on artificial neural network combined with clustering algorithms. Model was designed for each booth of the REDMAS and each pollutant separately. Resumen La contaminación atmosférica es una amenaza aguda, constituye un fenómeno que tiene particular incidencia sobre la salud del hombre. Los cambios que se producen en la composición química de la atmósfera pueden cambiar el clima, producir lluvia ácida o destruir el ozono, fenómenos todos ellos de una gran importancia global. La Organización Mundial de la Salud (OMS) considera la contaminación atmosférica como una de las más importantes prioridades mundiales. Salamanca, Gto., México; ha sido catalogada como una de las ciudades más contaminadas en este país. La industria de la zona propició un importante desarrollo económico y un crecimiento acelerado de la población en la segunda mitad del siglo XX. Las afectaciones en el aire son graves y se han hecho importantes esfuerzos por medir las concentraciones de los contaminantes. Las principales fuentes de contaminación son fuentes fijas como industrias químicas y de generación eléctrica. Los contaminantes que se han registrado como preocupantes son el Bióxido de Azufre (SO2) y las Partículas Menores a 10 micrómetros (PM10). La predicción de las concentraciones de estos contaminantes puede ser una potente herramienta que permita tomar medidas preventivas como reducción de emisiones a la atmósfera y alertar a la población afectada. En la presente tesis doctoral se propone un modelo de predicción de concentraci ón de los contaminantes más críticos SO2 y PM10 para cada caseta de monitorización de la Red de Monitorización Atmosférica de Salamanca (REDMAS). Los modelos propuestos plantean el uso de las variables meteorol ógicas como factores que influyen en la concentración de los contaminantes. La información utilizada durante el desarrollo de este trabajo corresponde a datos reales obtenidos de la REDMAS. En el Modelo Propuesto (MP) se aplican Redes Neuronales Artificiales (RNA) combinadas con algoritmos de agrupamiento. La RNA utilizada es el Perceptrón Multicapa con una capa oculta, utilizando estructuras independientes para la predicción de cada contaminante. Las variables meteorológicas disponibles para realizar la predicción fueron: Dirección de Viento (DV), Velocidad de Viento (VV), Temperatura (T) y Humedad Relativa (HR). Los algoritmos de agrupamiento K-means y Fuzzy C-means son utilizados para encontrar relaciones existentes entre los contaminantes atmosféricos en estudio y las variables meteorológicas. Dichas relaciones aportan información a las RNA para obtener la predicción de los contaminantes, la cual es agregada como entrada de las RNA. Los resultados del modelo propuesto en este trabajo son comparados con los resultados de una Regresión Lineal Multivariable (RLM) y un Perceptrón Multicapa (MLP). La evaluación de la predicción se realiza con el Error Medio Absoluto, la Raíz del Error Cuadrático Medio, el coeficiente de correlación y el índice de acuerdo. Los resultados obtenidos muestran la importancia de las variables meteorológicas en la predicción de la concentración de los contaminantes SO2 y PM10 en la ciudad de Salamanca, Gto., México. Los resultados muestran que el MP predice mejor la concentración de los contaminantes SO2 y PM10 que los modelos RLM y MLP. Los modelos implementados para cada caseta de monitorizaci ón tienen la capacidad para realizar predicciones de calidad del aire, estos modelos pueden ser implementados en un sistema que permita realizar la predicción en tiempo real y analizar el impacto en la salud de la población. Entre los principales resultados obtenidos del desarrollo de esta tesis podemos citar: Se propone un modelo basado en una red neuronal artificial combinado con algoritmos de agrupamiento para la predicción con una hora de anticipaci ón de la concentración de cada contaminante (SO2 y PM10). Se diseñó un modelo diferente para cada contaminante y para cada una de las tres casetas de monitorización de la REDMAS. Se propone un modelo de predicción del promedio de la concentración de las próximas 24 horas de los contaminantes SO2 y PM10, basado en una red neuronal artificial combinado con algoritmos de agrupamiento. Se diseñó un modelo para cada caseta de monitorización de la REDMAS y para cada contaminante por separado.

Evaluation of the influence of toll systems on energy consumption and CO2 emissions: A case study of a Spanish highway

This paper studies the energy consumption and subsequent CO2 emissions of road highway transportation under three toll systems in Spain for four categories of vehicles: cars, vans, buses and articulated trucks. The influence of toll systems is tested for a section of AP-41 highway between Toledo and Madrid. One system is free flow, other is traditional stop and go and the last toll system operates with an electronic toll collection (ETC) technology. Energy consumption and CO2 emissions were found to be closely related to vehicle mass, wind exposure, engine efficiency and acceleration rate. These parameters affect, directly or indirectly, the external forces which determine the energy consumption. Reducing the magnitude of these forces through an appropriate toll management is an important way of improving the energy performance of vehicles. The type of toll system used can have a major influence on the energy efficiency of highway transportation and therefore it is necessary to consider free flow.

E-comparisons: A new approach to round robin tests for environmental noise assessment in transport infrastructures

The increasing importance of pollutant noise has led to the creation of many new noise testing laboratories in recent years. For this reason and due to the legal implications that noise reporting may have, it is necessary to create procedures intended to guarantee the quality of the testing and its results. For instance, the ISO/IEC standard 17025:2005 specifies general requirements for the competence of testing laboratories. In this standard, interlaboratory comparisons are one of the main measures that must be applied to guarantee the quality of laboratories when applying specific methodologies for testing. In the specific case of environmental noise, round robin tests are usually difficult to design, as it is difficult to find scenarios that can be available and controlled while the participants carry out the measurements. Monitoring and controlling the factors that can influence the measurements (source emissions, propagation, background noise…) is not usually affordable, so the most extended solution is to create very effortless scenarios, where most of the factors that can have an influence on the results are excluded (sampling, processing of results, background noise, source detection…) The new approach described in this paper only requires the organizer to make actual measurements (or prepare virtual ones). Applying and interpreting a common reference document (standard, regulation…), the participants must analyze these input data independently to provide the results, which will be compared among the participants. The measurement costs are severely reduced for the participants, there is no need to monitor the scenario conditions, and almost any relevant factor can be included in this methodology

Prediction of PM10 concentrations using Fuzzy c-Means and ANN

Salamanca has been considered among the most polluted cities in Mexico. The vehicular park, the industry and the emissions produced by agriculture, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Particulate Matter less than 10 μg/m3 in diameter (PM10). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables (wind speed, wind direction, temperature and relative humidity) and air pollutant concentrations of PM10. Before the prediction, Fuzzy c-Means clustering algorithm have been implemented in order to find relationship among pollutant and meteorological variables. These relationship help us to get additional information that will be used for predicting. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of PM10 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results shown that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours

Environmental analysis of residential building facades through energy consumption, GHG emissions and costs

This paper provides some results on the potential to minimize environmental impacts in residential buildings life cycle, through façade design strategies, analyzing also their impact on costs from a lifecycle perspective. On one hand, it assesses the environmental damage produced by the materials of the building envelope, and on the other, the benefits they offer in terms of habitability and liveability in the use phase. The analysis includes several design parameters used both for rehabilitation of existing facades, as for new facades, trying to cover various determinants and proposing project alternatives. With this study we intended to contribute to address the energy challenges for the coming years, trying also to propose pathways for innovative solutions for the building envelope.

Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico

Salamanca is cataloged as one of the most polluted cities in Mexico. In order to observe the behavior and clarify the influence of wind parameters on the Sulphur Dioxide (SO2) concentrations a Self-Organizing Maps (SOM) Neural Network have been implemented at three monitoring locations for the period from January 1 to December 31, 2006. The maximum and minimum daily values of SO2 concentrations measured during the year of 2006 were correlated with the wind parameters of the same period. The main advantages of the SOM Neural Network is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. For each monitoring location, SOM classifications were evaluated with respect to pollution levels established by Health Authorities. The classification system can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.