Revealed Willingness-to-pay for Reduction of Air Pollution Risks in Urban China

Guodong Sun, H. Keith Florig

Guodong Sun
H. Keith Florig, Department of Engineering and Public Policy, Carnegie Mellon University

China is under mounting pressure from industrialized nations to abate greenhouse gas (GHG) emissions. Proponents of GHG control for China often cite reduction in the health risks from local air pollution as a significant ancillary benefit of GHG controls. This research will estimate the value of such health benefits by examining investments that China has already made in particulate air pollution control (independent of GHG policy), as an indication willingness-to-pay for risk reductions from air pollution. Because these investments are expected to increase with the level of economic development, pollution control measures will be examined both longitudinally (over the past 20 years) and cross-sectionally (in several cities at different stages of economic development). For each city studied, we plan to 1) review published regulations and interview local officials to develop a chronology of the implementation of air pollution control measures, 2) choose several control measures with quantifiable costs and emissions reductions, and 3) estimate the cost-effectiveness for risk reduction of each control measure by combining cost and emission information with models of population exposure and dose-response, 4) compare the resulting estimates of willingness-to-pay to level of economic development.

Using Total Exposure to Rank Sources of Air Pollution in China
Guodong Sun, H. Keith Florig

Improved air quality is an important ancillary benefit of GHG emission reduction. Given the huge health damages from air pollution in China, this benefit can be substantial. The health benefits of GHG emissions reductions from a particular source of air pollution depends not only on pollutant emission reductions, but also on the proximity of populations to the source. Thus, GHG emissions reductions applied to sources in the midst of dense urban centers (e.g. diesel buses, coal-fired residential boilers) may have greater health benefits than GHG reductions applied to more distant, but larger sources, such as power plants.

This project examines this issue by analyzing and ranking the major sources of non-occupational exposure to particulate air pollution (primary PM10) in China. Models of pollutant emissions, pollutant diffusion, population location, and population time-use are developed to estimate total PM10 exposure for five major source types and four population subgroups in both urban and rural settings. Particle sources addressed are environmental tobacco smoking (ETS), cooking stoves, heating stoves, district heating boilers, on-road vehicles, and coal-fired power stations. Population subgroups considered are pre-school children, school children, working adults, and the aged. This study is based on a hypothetical city and a hypothetical village with characteristics typical of China's urban and rural areas. The models are coded in Analytica so that uncertainties and variabilities in model parameters can be easily considered in the results, which are expressed as probability distributions. For urban areas in northern China, the model shows that residential heating is the most important source of non-occupational population exposure to primary PM10 for all population groups. This is followed by ETS, cooking, on-road vehicles, and power stations. Urban exposures from heating, ETS, and cooking are at least one order of magnitude higher than exposures from motor vehicles and power stations, yet power station emissions often receive the most regulatory attention. In rural areas, cooking and heating are the largest sources of primary PM10 exposure with ETS making a significant contribution as well. Except for cooking, there is little source-to-source variation in among age groups. Individual exposure to cooking by pre-school children and aged-people are about 45% and 30% higher than that by school children and working people, respectively.