Investigation of Climate Change Impacts on Prairie's Petroleum Industries in Canada
Bibliographic record
Abstract
Abstract Climate change will lead to a number of direct and indirect impacts on petroleum industries in Canada's prairie provinces. A challenging question facing this industry is how they should adapt to the changing climatic conditions in order to maintain or improve their economic and environmental efficiencies. In this paper, a questionnaire-based survey was conducted to obtain attitudes of various stakeholders towards climate change impacts and corresponding adaptation measures to the petroleum industries, and a Chi-square (Χ2) statistical test was implemented to examine complex interactions of the survey results. Several petroleum-related processes and activities that are vulnerable to climate change are analyzed. The results provide useful bases for decisions of climate-change adaptation in the prairies' petroleum industries. Introduction The petroleum industries are critical components of the economies in the three Prairie's provinces (Alberta, Saskatchewan, and Manitoba). In fact, petroleum industrial activities in Canada are concentrated in the Prairies. For example, Alberta accounts for about 78% of the total hydrocarbon production in Canada, while Saskatchewan and Manitoba are areas of growing petroleum production activities. In addition, a number of refineries are located in the prairies, and they are the major part of the Canadian petrochemical industry. However, the petroleum industries may potentially be vulnerable to the changing climate (1), and serious challenges facing the industries may come from not only the possible impacts of climate change but also the socioeconomic consequences of adaptation strategies proposed to respond to climate change(2). Under changing climate conditions, many aspects of the petroleum industries will be affected (3). For instance, the hotter and longer summer can result in decreased available surface water for use, and the mountain glaciers which are major sources of water in the Prairies, may also be melted due to increased temperature and thus result in reduced water supply, as a result, the water availability of the petroleum industry is very sensitive to changes in climate(4). Changes in climate can also affect the production infrastructures, for example, receding permafrost in the Prairies due to the warming climate may lead to increased slope instability and soil erosion and thus affect safety of pipelines, while higher humidity in the atmosphere would enhance corrosion of metal-built equipment. Due to the severe sensitivities to climate change, the impact analysis and adaptation planning will be crucial in the effort to improve the economic and environmental efficiencies(5,6). The implementation of appropriate adaptation strategies is the desired outcome of careful impacts analysis. Nevertheless, climate change brings threats as well as opportunities, and future benefits can result from better adapting to climate variability and extreme atmospheric events, while significant costs may occur from maladaptive policies and practices. Concerns about climate change and the challenges it poses will require sustained efforts to develop understanding and effective solutions while at the same time achieving the objectives of economic development for the petroleum industries. As a result, the development of petroleum industries in the Prairie's provinces should consider the possible effects of climate change and corresponding adaptation strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".