Air pollution kills more people than AIDS and malaria combined, and climate change is one of the biggest threats to human survival and well-being in the twenty-first century. Motivated by these problems, Dr. Shen’s research explores how incentives shape environmental politics in developing countries, especially China.
Shen, S. V. The Political Pollution Cycle. Abstract
Incentives shape political behavior. This paper shows that even after controlling for institutional factors and macro trends, local policy implementation in autocracies like China can change over time in potentially predictive ways. Studying the critical case of air pollution control policies, I advance a theory of what I call the “political pollution cycle” to fathom the effect of political incentives on local policy implementation over time. I theorize that local leaders cater to the policy prioritization of the center and, in the process, foster systematic regional patterns of air quality over time. Using remote sensing, box modeling, observational data, and qualitative field research, I find that top prefectural leaders in China ordered laxer regulation of pollution towards the end of their tenure so that the delivery of social stability and economic achievements boded well for their career advancement. Such regulatory forbearance came unintentionally with tremendous human costs.
- Winner of the 2017 Paul A. Sabatier Award. Award by the American Political Science Association for the best conference paper on science, technology, and environmental politics.
- Winner of the 2018 Malcolm Jewell Award. Award by the Southern Political Science Association for the best overall graduate student conference paper.
Shen, S. V. Local Political Regulation Waves.
Shen, S. V., Martinez, C., & Franco, E. When Electoral Responsiveness Harms Voters: Evidence from Electoral Pollution Cycles in Mexico’s Metropolitan Areas.
Kopas, J., York, E., Jin, X., Harish, S. P., Kennedy, R., Shen, S. V., & Urpelainen, J. Environmental Justice in India: Incidence of Air Pollution from Coal-Fired Power Plants (R & R, Ecological Economics).
Shen, S. V. (2019). Pricing Carbon to Contain Violence. The First International Research Conference on Carbon Pricing. Washington, D.C.: The World Bank. Abstract
Violence is destructive to social order, economic growth, and the human condition. The annual total cost of violence is estimated to be 11 percent of the world’s GDP. However, violence has rarely made its way into economic models. In the meantime, increasing scientific evidence points to an active link between climate change and the incidence of interpersonal and intergroup violence. This study connects the climate–economy and the climate–violence systems by putting forth a new method to internalize the costs of climate-induced violence in the established MERGE integrated assessment model. It finds that such internalization can double the optimal carbon price, a relationship that holds across different specifications regarding climate sensitivity, GDP growth rate, and the willingness to pay (WTP) to avoid nonmarket climate damages. Normatively, under the realistic assumption that the WTP is at 1 percent of regional income, the avoided costs from climate-induced violence in sub–Saharan Africa is modeled to reach 3.7 percent of the region’s GDP in 2200, a very significant figure for an area that is already riddled with underdevelopment and violence. The approach of this paper is a first for the modeling community, indicating directions for future research. For the policy community, this paper takes recent econometric findings to the next step toward understanding required for decisions.
Shen, S. V., Cain, B. E., & Hui, I. (2019). Public Receptivity in China towards Wind Energy Generators: A Survey Experimental Approach. Energy Policy, 129, 619 – 627. [PDF] Abstract
China leads the world’s wind energy market, but little has been written about public receptivity towards wind energy generators in China. To fill this gap, we pursue a survey experimental approach to examine explanations for receptivity based on evidence from OECD countries as well as the importance of public knowledge in augmenting public acceptance of wind energy generators in China. We find that Chinese respondents are sensitive to turbine siting near their residences, to cost considerations when imposed on them directly, to wildlife externalities, and to noise from turbines. Interestingly, Chinese respondents seem to be concerned about radiation, a finding unprecedented in the literature, and are less assured by scientific assurances that radiation is not a problem. Instead, the Chinese central government is best suited to address concerns about this topic. Targeted information provision to the public can improve public knowledge about aspects of wind energy of concern. Hence, the Chinese central government can promote wind energy deployment not just because it is an authoritarian government determined to get things done, but also because it can provide relevant information to reduce potential public resistance.
Liao, X., Shen, S. V., & Shi, X. (2019). The effects of behavioral intention on the choice to purchase energy-saving appliances in China: the role of environmental attitude, concern, and perceived psychological benefits in shaping intention. Energy Efficiency. [PDF] Abstract
Purchasing energy-saving appliances is a sensible and practical way to reduce carbon emissions from the residential sector in China. This study examines the relationship between pro-environment behavioral intention—undergirded by environmental attitude and concern as well as perceived psychological benefits—and the choice to purchase energy-saving appliances among Chinese households. Integrating psychological benefits (i.e., warm glow and self-express benefits) into the theory of planned behavior, a first of its kind for China, we designed and implemented a cross-sectional online survey in 2016. We conducted Probit regression analyses based on the 942 effective responses collected. The results reveal that behavioral intention has significantly positive effects on the choice to purchase energy-saving appliances. Environmental attitude and concern, as well as psychological benefits, have a significantly positive impact on respondents’ behavioral intention to buy energy-saving devices. Also, age and household size significantly and positively correlate with purchasing energy-saving appliance decision. These results point to useful policy implications to boost consumer support for energy-saving appliances in China and provide a foundation for similar research in other developing contexts.
Shao, Q., & Shen, S. V. (2017). When Reduced Working Time Harms the Environment: A Panel Threshold Analysis for EU-15, 1970-2010. Journal of Cleaner Production, 147, 319 – 329. [PDF] Abstract
Conventional wisdom has it that less working time is good for mitigating environmental pressure. Only a few studies have documented contradictory evidence. In this paper, we use panel threshold model, which is arguably the first of its kind in environmental analysis, to further document nonlinear relationships between working time and environmental pressure in EU-15 countries between 1970 and 2010. We find that the sign of this relationship shifts from positive to negative, as the working hours per worker decreases; France, Denmark, Germany, and the Netherlands experienced more environmental pressure with shorter working week. To the backdrop of reduced working time during our research period, our paper sheds new light on the traditional view of “the less, the better,” as curtailing working time beyond certain thresholds may inadvertently incur exacerbation of environmental pressure.
Shen, S. V. Using Machine Learning to Find Environmentally At-Risk Communities. Abstract
Environmental health persists as a genuine concern in many US localities. However, public agencies often face limited capacity and resources to collect comprehensive environmental health data. Inspired by CalEnviroScreen, an environmental health assessment tool used to identify environmentally at-risk communities in California, I calculate pollution burden scores at the census tract level for the entire contiguous United States. Pollution burden is a composite score that encompasses 12 environmental (air, water, waste) indicators. I combine the actual pollution burden indicator data with predicted statistics using machine learning. I create an interactive, publicly accessible National Pollution Burden Map using ArcGIS Online. Although applied to US states, the same approach can also be applied to other regions of the world.