China currently faces critical challenges in the energy sector. Although it is the world’s largest carbon emitter—making it a central player in the effort to mitigate climate change—it has pledged to achieve a dramatic turnaround by peaking emissions in 2030. Furthermore, despite the need to continue to produce cheap electricity for its development, the country has been beset by deteriorating air quality, resulting in public outcry. To move forward, China will need to consider how to balance the tradeoffs of providing low-cost electricity, reducing air pollution, and meeting its climate goals.
In this work, we present an ongoing study in which we design and administer a survey with a discrete choice experiment (DCE) to assess how individuals in China make tradeoffs between climate, health, and economic consequences when evaluating electricity generation scenarios. In this online DCE, individuals face a series of screens and are asked in each screen to choose one of two alternatives. Each of these alternatives is characterized by four distinct attributes: the electricity generation portfolio of their province (i.e. percentage coming from renewables, nuclear power, and fossil fuels), their monthly electricity bill, and changes to carbon dioxide (CO2) and sulfur dioxide (SO2) emissions levels. Each individual sees 16 of these comparison screens, with various attribute combinations for the different alternatives selected from a subset of all possible combinations.
We will evaluate respondents’ preferences for each of the survey attributes using a mixed logit random utility model. This model will be used to assess respondents’ preferences in terms of probability of support for various attribute combinations, as well as willingness-to-pay for emissions reductions. In addition, we will test for interactions with observed, daily air quality to see whether respondents’ preferences for emissions reductions correlate with actual pollution levels. Interactions using various temporal aggregations of the air quality data will be tested to assess whether correlations are characterized by existing social-psychological theories on availability heuristics, such as evaluation by moments and the peak-end rule.
To date, we have already designed the survey and conducted a small-scale pilot test involving 50 people in Beijing and Shanghai. We next plan sample 1000 participants recruited from public spaces across 10 cities in China, which we hope to complete in March.