While carbon pricing is widely seen as a crucial element of climate policy and has been implemented in many countries, it also has met with strong resistance. We provide a comprehensive overview of public perceptions of the fairness of carbon pricing and how these affect policy acceptability. To this end, we review evidence from empirical studies on how individuals judge personal, distributional and procedural aspects of carbon taxes and cap-and-trade. In addition, we examine preferences for particular redistributive and other uses of revenues generated by carbon pricing and their role in instrument acceptability. Our results indicate a high concern over distributional effects, particularly in relation to policy impacts on poor people, in turn reducing policy acceptability. In addition, people show little trust in the capacities of governments to put the revenues of carbon pricing to good use. Somewhat surprisingly, most studies do not indicate clear public preferences for using revenues to ensure fairer policy outcomes, notably by reducing its regressive effects. Instead, many people prefer using revenues for ‘environmental projects’ of various kinds. We end by providing recommendations for improving public acceptability of carbon pricing. One suggestion to increase policy acceptability is combining the redistribution of revenue to vulnerable groups with the funding for environmental projects, such as on renewable energy.
Key policy insights
If people perceive carbon pricing instruments as fair, this increases policy acceptability and support.
People’s satisfaction with information provided by the government about the policy instrument increases acceptability.
While people express high concern over uneven distribution of the policy burden, they often prefer using carbon pricing revenues for environmental projects instead of compensation for inequitable outcomes.
Recent studies find that people’s preferences shift to using revenues for making policy fairer if they better understand the functioning of carbon pricing, notably that relatively high prices of CO2-intensive goods and services reduce their consumption.
Combining the redistribution of revenue to support both vulnerable groups and environmental projects, such as on renewable energy, seems to most increase policy acceptability.
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When travelling, people are accustomed to taking and uploading photos on social media websites, which has led to the accumulation of huge numbers of geotagged photos. Combined with multisource information (e.g. weather, transportation, or textual information), these geotagged photos could help us in constructing user preference profiles at a high level of detail. Therefore, using these geotagged photos, we built a personalised recommendation system to provide attraction recommendations that match a user's preferences. Specifically, we retrieved a geotagged photo collection from the public API for Flickr (Flickr.com) and fetched a large amount of other contextual information to rebuild a user's travel history. We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation (the matching process) and candidate ranking. In the matching process, we used a support vector machine model that was modified for multiclass classification to generate the candidate list. In addition, we used a gradient boosting regression tree to score each candidate and rerank the list. Finally, we evaluated our recommendation results with respect to accuracy and ranking ability. Compared with widely used memory-based methods, our proposed method performs significantly better in the cold-start situation and when mining ‘long-tail’ data. 相似文献