On the assessment of the CO2 emissions from the industrial sector : the role of energy and exergy analysis methods, an approach to enhance sustainable strategies
Notice bibliographique
Résumé
Growing population, rapid urbanization and technological advancements have resulted in increasing energy demand. During the last 50 years, societies around the world have been transforming in a faster and incessant way. The growth of the population has resulted in the generation of mega-cities; at the same time, the economic growth of these areas entails consumption of goods. It is challenging the availability of natural resources; as a result, the relationship of the industrial unit with its urban environment has been changing in the pursuit to create the minimum possible impacts to the landscape. \n \nThis constant increase in population, gross domestic product and exports in the last decades has resulted in the growth of the manufacturing industry and the transportation of goods. Globally, the industrial sector remains as one of the three main consumers of fossil fuels; hence, it is one of the prime sources of greenhouse gases (GHG), resulting on environmental and health problems. Particularly in the North American region, the industrial sector embodies about 50% of the total energy consumption and more than 30% of the total carbon dioxide (CO2) emissions. \n \nA simple definition of exergy affirms that exergy is the energy that is available to be used. Some applications of exergy include resource accounting, energy conservations, complex systems analysis and efficiency improvements. The general objective of this research was to validate the suitability of exergy analysis, as a tool to assist decision makers in the design for future energy and environmental policy, both in public and private institutions as an approach to enhance sustainable strategies. To explore the appropriateness of this indicator, a geographical approach was applied to analyze three different geographic levels (global, regional and local). Additionally, analyze the main drivers of CO2 emissions, within the framework of the environmental Kuznets curve (EKC) hypotheses, including exergy indicators. \n \nIn order to explore the appropriateness of exergy analysis, new indicators of CO2 emissions (energy-exergy consumption and energy-exergy efficiencies) were introduced, with the goal to be compared to traditional indicators of CO2 emissions (gross domestic product, energy intensity, carbon intensity, trade openness and human development index) to study their suitability as indicators. \n \nResults of these thesis gives recommendations on how to apply exergy analysis on a large scale level (societal sectors) and to provide the tools necessary for exergy analysis, using this data, so as to better be applicable to this particular industrial sector. At global level, the results shows high correlation between CO2, GDP, energy consumption, energy intensity and trade openness; but not statistically significant values for trade openness and energy intensity. At regional level, granger Causality was found from proposed exergy variables in the USA and Mexico to CO2 emissions; also causality was detected in Canada and the US from trade openness to CO2 emissions. Finally, at the local level (study case), poor exergy efficiency is still occurring in the Mexican industrial sector, compared with developed countries. \n \nThe outcomes of the applied methodological approach conducted for the three geographical levels proposed in the thesis, confirms the suitability of exergy methods as a tool to assist the design of energy and environmental policy, both in public institutions or private corporations as an approach to enhance sustainable strategies. Particularly, for policymakers of the three NAFTA countries, exergy proves value due the current impasse of negotiations, not only for tackling CO2 emissions, but also for promoting growth in the renewable energy share. The addition of the exergetic indicator provides an interesting insight on energetic and environmental strategies. This thesis showed the need to speed de-carbonization processes; it was demonstrated that the exergy analysis method provides a non-traditional approach in the right to reduce GHGs and contribute to sustainable development.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,005 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».