Community level emission reduction with carbon capturing : a life cycle thinking based approach
Bibliographic record
Abstract
The global climate is being heavily affected by greenhouse gas (GHG) emissions, the most significant of which is carbon dioxide (CO₂). According to the Pan-Canadian framework on clean growth and climate change, Canada has set ambitious targets to realize a low carbon future. Amongst the available emission reduction strategies, on-site carbon capturing, storage, and utilization (CCSU) have proven their potential to reduce CO₂ emissions from large-scale industrial facilities. However, the integration of CCSU technologies with community-scale energy generation applications such as district energy systems has not been explored sufficiently in literature. Evaluation of the applicability of these novel technologies should not be limited to technical and economic criteria, but should also be extended to environmental and societal aspects. The objective of this study is to propose a framework to compare and prioritize emission reduction strategies that include CCSU and renewable energy technologies to develop zero-emission communities. The study follows a multi-stage approach. Initially, the CCSU technologies that are technically viable for medium to large scale energy systems were selected using a hybrid rule-based and data-envelopment assessment. A life cycle thinking-based decision support framework was developed. It incorporates a multi-criteria decision-making approach to rank and prioritize community energy emission mitigation strategies. A scenario-based method was employed to assess the performance of selected CCSU technologies along with other compatible alternative energy choices. Moreover, a system dynamic modeling approach was employed to assess the long-term economic feasibility of CCSU technologies. The framework was demonstrated for nine provinces in Canada. The optimum emission reduction strategy for regions relying heavily on renewable sources came out to be grid and natural gas – district heating energy supply coupled with onsite solar energy. Regions with high dependence on fossil fuel energy sources performed better with CCSU in combination with grid, natural gas, and onsite solar energy supply. Moreover, based on the system dynamics model results, CCSU is more feasible in provinces with high reliance on fossil fuel energy sources. The findings from this study are geared towards providing useful decision-support tools for policy experts, investors, and utility providers who are responsible for policy and investment decisions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".