China’s Cap on Coal Consumption (CCC) Policy serves as a key strategy to address the serious air pollution in China, and it helps to address coal’s climate, environment and health damages. Current implementation of it focuses on substituting coal used in power plants and boilers with natural gas, whereas phasing out household coal use is less emphasized. This study estimates the benefits and costs of interventions for phasing out coal used in power plants and in households in Beijing. The results suggest that the phasing out of household coal use can result in net social benefits. However, coal-to-gas projects for power plants actually bring net social losses, a result largely attributable to the relative high price of natural gas in China. In addition to the actual policy evaluations of phasing out coal, this study outlines how to conduct economic analysis of air pollution policies in China taking into account uncertainty and correlations of key parameters. With the importance at a national and global level to reduce the negative effects of coal consumption, together with the trend of scaling up coal reduction interventions in China from local pioneers to the national level, this study provides implications on how to achieve more socially beneficial results for such interventions.

Regulatory impact analyses (RIAs) weigh the benefits of regulations against the burdens they impose and are invaluable tools for informing decision makers. We offer 10 tips for nonspecialist policymakers and interested stakeholders who will be reading RIAs as consumers.

1. 1. Core problem: Determine whether the RIA identifies the core problem (compelling public need) the regulation is intended to address.

2. 2. Alternatives: Look for an objective, policy-neutral evaluation of the relative merits of reasonable alternatives.

3. 3. Baseline: Check whether the RIA presents a reasonable “counterfactual” against which benefits and costs are measured.

4. 4. Increments: Evaluate whether totals and averages obscure relevant distinctions and trade-offs.

5. 5. Uncertainty: Recognize that all estimates involve uncertainty, and ask what effect key assumptions, data, and models have on those estimates.

6. 6. Transparency: Look for transparency and objectivity of analytical inputs.

7. 7. Benefits: Examine how projected benefits relate to stated objectives.

8. 8. Costs: Understand what costs are included.

9. 9. Distribution: Consider how benefits and costs are distributed.

10. 10. Symmetrical treatment: Ensure that benefits and costs are presented symmetrically.

The value of small changes in mortality risks, generally expressed as the value per statistical life, is an important parameter in benefit-cost analysis. However, little is known about the values held by populations in low- and middle-income countries. This article introduces a symposium that includes three additional articles which explore related theory and research.

Extrapolation of estimates of the value per statistical life (VSL) from high- to low- or middle-income populations requires attention to the possible effects of differences in income, current mortality risk, health, life expectancy, and many other factors. The standard theoretical model of VSL implies that VSL increases with income and decreases with current mortality risk. The effect of mortality risk is likely to be negligible while the effect of income is large and poorly quantified. Effects of differences in life expectancy and health are theoretically ambiguous. Effects of other factors, including differences in health care, formal and informal support networks, and cultural or religious factors that affect preferences for spending on oneself or others may be important but are unknown. Practical issues include choice of the most appropriate measure of income and possible differences in the patterns of age dependence between populations.

Countries throughout the world use estimates of the value of a statistical life (VSL) to monetize fatality risks in benefit-cost analyses. However, the vast majority of countries lack reliable revealed preference or stated preference estimates of the VSL. This article proposes that the best way to calculate a population-average VSL for countries with insufficient or unreliable data is to transfer a base VSL from the United States calculated using labor market estimates from Census of Fatal Occupational Injuries data, coupled with adjustments for differences in income between the United States and the country of interest. This approach requires estimation of two critical inputs: a base U.S. VSL and the income elasticity of the VSL. Drawing upon previous meta-analyses that include adjustments for publication selection biases, we adopt a base VSL of $9.6 million. We utilize a sample of 953 VSL estimates from 68 labor market studies of the VSL covering fourteen lower-middle income to high income nations. We estimate the income elasticity of the VSL within the United States to be from 0.5 to 0.7 and to be just above 1.0 for non-U.S. countries. Quantile regression reveals that much of the disparity in income elasticities is attributable to income differences between the United States and other countries, as the income elasticity increases for lower income populations. Using income classifications from the World Bank, we calculate average VSLs in lower income, lower-middle income, upper-middle income, and upper income countries to be $107,000, $420,000, $1.2 million, and $6.4 million, respectively. We also present VSL estimates for all 189 countries for which World Bank income data are available, yielding a VSL range from $45,000 to $18.3 million.

This study is the eighth in a series of stated-preference studies designed to enhance the basis for international benefits transfer of value of statistical life (VSL) estimates. The series has fielded essentially similar stated-preference surveys in Canada, China, France, Italy, Japan, Mongolia, the United Kingdom, and the United States. This Chinese study estimates the willingness to pay for contemporaneous and future mortality risk reductions of residents of Shanghai, Jiujiang, and Nanning, China using a stated-preference payment-card survey. The pooled VSL for a contemporaneous reduction in annual mortality risk reduction of 5 in 10,000 is about 1.47 million 2009 yuan ($614,805 U.S. $2016), with income elasticities of 0.2 to 0.25. This VSL estimate is at the lower end of estimates from the eight countries, between those from Mongolia and Japan, and in the mid-range of estimates of willingness to pay (WTP) for mortality risk reductions as a percentage of household income. We find lower discount rates in the Mongolia, Japan, and China studies than in those fielded in North America or Europe. The study also explores the relative performance of dichotomous choice and stated-preference card elicitation methods in a middle income country setting and develops a computerized “payment card” that allows testing for anchoring. Implicit transfer elasticities across countries, calculated using the VSLs we estimate and each country’s income, relative to those of the United States, yields estimates of 0.88–0.95 for the lower income countries. These compare with the default assumption of 1.0 or assumed elasticities of 1.2 for developing countries.