Investigation of Climate Change Impacts on Prairie's Petroleum Industries in Canada
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».