4 research outputs found
Is digitalization leading to CO2 emission cutting? /
No full textPurpose – Our article aims to contribute to the main research question of whether digitalization can be used to mitigate carbon emissions. One of the main challenges in capturing the effects of digitalization on carbon emissions lies within the measurement. Design/methodology/approach – We create six proxiesto measure digitalization thatrepresent the dynamics of the ICT sector, relative size, relative business expenditures of R&D in the ICT sector, the relative imports and exports of ICT goods and relative digital capital. We perform OLS regression on a sample covering 26 European Union countries during the time period 2003–2019. To add statistical robustness, we perform the quantile panel regression. Findings – Ourresultsshow that the relative size ofthe ICTsector and digital capital have a neutral impact on the country’s carbon emissions. An increase in ICT imports of goods and ICT exports of goods as a ratio of the overall country’s imports and exports, on the other hand, could lead to an increase in carbon emissions. On the other hand, the net trading balance of ICT goods (ICT exports minus ICT imports) in our data set for EU countries lowers carbon emissions. Our results provide no conclusive evidence for a relationship between business expenditures on R&D in the ICT sector and carbon emissions. Originality/value – We contribute to existing literature by creating new measurements to capture digitalization and identifying which digitalization aspects either enhance or diminish carbon emissions, and we apply this approach to the European Union based on 26 countries for the period of 2003–2019
Carbon Emissions Reduction and Corporate Financial Performance:The Influence of Country-Level Characteristics
No full textUsing a cross-country dataset covering 9265 observations on 1785 firms representing 53 countries over the period 2004–2019, this study investigates the relation between carbon emissions reduction and corporate financial performance (CFP). We perform OLS regressions with fixed effects. We found that carbon emissions reduction increases the return on assets, the return on equity, and the return on sales, whereas it has no effect on the Tobin’s Q and the current ratio. The positive relationship with the return on assets is stronger for firms with a higher responsibility score. We study country characteristics by modeling GDP growth, overall emissions within a country, and the presence of carbon emissions legislation. Our results indicate that the overall carbon emissions of a country and the presence of carbon emissions legislation are related to both corporate carbon emissions reduction and CFP. Moderating effects of the country’s overall emissions and the presence of carbon emissions legislation do not affect the relationship between carbon emissions reduction and CFP. Despite the further understanding gained, the issue of whether it “pays to be green” can still not be resolved well.<br/
Sector and Country Effects of Carbon Reduction and Firm Performance
No full textPrevious studies have indicated a positive association between carbon reduction and firm performance. Using a dataset covering firms across 10 sectors and 53 countries over the period 2004–2019, we add to the literature by showing the differences between sectors and various groupings of countries on carbon reduction and firm performance in terms of the return on assets, the return on equity and the return on sales, as well as the Tobin’s Q and the current ratio. The services sector shows a positive result in relation to most of the corporate financial performance variables. The results also provide evidence for a negative relationship for agricultural and mining firms. The findings indicate that differences in carbon reduction are limited when allowing for various ways of grouping countries
Estimation of ammonia emissions in poultry sheds, using an artificial neuronal model
No full textIlustraciones, fotografías, tablasEn los últimos años la industria avícola ha experimentado tanto a nivel mundial como en Colombia un importante crecimiento, lo cual unido a la exigencia de la normatividad ambiental vigente
conlleva al control y disminución de las emisiones de amoniaco dentro de los galpones. El amoniaco proveniente de las deyecciones puede afectar a las aves y a las personas generando
problemas de salud y pérdidas económicas en dicha industria. La cantidad de la emisión de amoniaco depende de las condiciones ambientales y de diversas variables de diseño y operación
presentes en las actividades avícolas. En este trabajo, para la predicción de la emisión de
amoniaco liberadas al aire dentro de galpones para aves de postura y engorde, se elaboraron
redes neuronales artificiales multicapa feedforward – backpropagation, evaluando su desempeño
a partir del coeficiente de correlación lineal R. Posteriormente, se usó la red neuronal seleccionada y entrenada para analizar la influencia de diferentes variables en la generación de
emisiones de NH3. Se analizaron las variables ambientales; temperatura, humedad relativa, pH,
flujo de aire. Las variables de operación; densidad, tiempo de acumulación del estiercol, consumo
de proteína, raza de las aves y factores de diseño como el tipo de ventilación, tipo de galpón y
tipo de cama, se sugirieron algunas condiciones de operación para reducir las emisiones de NH3.
Los resultados de correlación entre las emisiones reales y estimadas por la red neuronal (R=0.99), muestran que la herramienta computacional desarrollada es confiable en la predicción de las emisiones y abre una agenda futura para la optimización y diseño de ambientes controlados
mediante aprendizaje de máquinas basadas en redes neuronales (texto tomado de la fuente).In the last years, the poultry industry has experienced significant growth both worldwide
and in Colombia, which together with the requirement of current environmental regulations
leads to the control and reduction of ammonia emissions within the sheds. Ammonia from
manure can affect birds and people, causing health problems and economic losses in this
industry. The amount of ammonia emission depends on environmental conditions and
various design and operating variables present in poultry activities. In this work, for the
prediction of the ammonia emission released into the air within sheds for laying and
fattening birds, feedforward - backpropagation multilayer artificial neural networks were
elaborated, evaluating their performance from the linear correlation coefficient R. used the
selected and trained neural network to analyze the influence of different variables on the
generation of NH3 emissions. The environmental variables were analyzed; temperature,
relative humidity, pH, air flow. The operation variables; Density, manure accumulation
time, protein consumption, breed of birds and design factors such as type of ventilation,
type of house and type of litter, some operating conditions were suggested to reduce NH3
emissions. The correlation results between the real and estimated emissions by the neural
network (R = 0.99), show that the computational tool developed is reliable in the prediction
of emissions and opens a future agenda for the optimization and design of controlled
environments through learning of machines based on neural networks.
Keywords: droppings; ammonia; environmental conditions; poultry industry; artifiMaestríaMagíster en Ingeniería - Ingeniería AmbientalSe realizó una revisión sistemática de la literatura sobre estudios de emisiones de
amoniaco en galpones avícolas tanto para aves de engorde como de postura. Se
analizaron 280 artículos de la base de datos científica Scopus. Fueron recopilados los
datos experimentales de estos estudios para países tanto del trópico como de la latitud
norte con diversas condiciones ambientales. Con los datos de los estudios se construyó
una base de datos estandarizada, obteniéndose un conjunto de datos general de 380
registros con estudios desde el año 1995 hasta la actualidad (tabla tal) . Posteriormente
se realizaron dos subconjuntos para el entrenamiento de las redes.
La efectividad de cada modelo como método de estimación de las emisiones de NH3, fue
evaluada utilizando un indicador estadístico que comúnmente se utiliza en la evaluación
del desempeño de las redes neuronales artificiales.Línea de investigación de Monitoreo, modelación y gestión de recursos naturale
