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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".