Selected Works in Progress

Shen, S. V. Endogenizing the Costs of Climate-Induced Violence in the Optimal Management of the Climate: A MERGE Modeling Approach (Revise & Resubmit). Abstract

Violence is destructive to social order and economic growth; it is a part of and can be devastating to 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, scientific evidence points to an active link between climate change and the incidence of interpersonal and inter-group 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 0.5 percent of the region’s GDP in 2050, 2 percent in 2100, and almost 4 percent in 2200, which are very significant figures 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.

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 (Revise & Resubmit). Abstract

Air pollution is a vexing problem for emerging countries that strike a delicate balance between environmental protection, health, and energy for growth. We examine these difficulties in a study of disparate levels of exposure to pollution from coal-fired power generation in India, a country with high levels of air pollution and large, marginalized populations. With data on coal plant locations, atmospheric conditions, and census demographics, we estimate exposure to coal plant emissions using models that predict particle transportation. We find that ethnic and poor populations are more likely to be exposed to coal pollution. However, this relationship is sometimes non-linear and follows an inverted u-shape similar to that of an Environmental Kuznets Curve. We theorize that this non-linear relationship is due to the exclusion of marginalized communities from both the negative and positive externalities of industrial development.

Shen, S. V. The Political Pollution CycleAbstract

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. Abstract

Can electoral responsiveness harm voters, and if so, when? Most of the literature on electoral responsiveness focuses on electoral returns to incumbents and assumes that voters support policies that have only positive effects. In this paper, we theorize the circumstances when a preferred policy can also have adverse consequences for voters.  We provide evidence of the effect of electoral incentives on air quality over time in a young, developing democracy – Mexico. By leveraging on the exogeneity of the local electoral calendar, we find that state governors are particularly incentivized to cater to voter preferences around the election. They do so by promoting economic activities that have short-term electoral returns but sacrifice air quality. We call this phenomenon an “electoral pollution cycle,” and its effect is most pronounced in the transportation sector. The incentive to induce an active cycle is influenced by high electoral competition, middle-class representation of the political party, and the politician’s old age. The electoral pollution cycle imposes significant human costs. This paper contributes to the study of electoral incentives in unconsolidated democracies. It also sheds light on how voters fail to internalize the tradeoff between economic growth and environmental quality.

Peer-Reviewed Publications

Liao, X., Shen, S. V., & Shi, X. (2020). 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, 13 (1), 33 – 49. [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. 

Shen, S. V., Cain, B. E., & Hui, I. (2019). Public Receptivity in China towards Wind Energy Generators: A Survey Experimental ApproachEnergy Policy, 129, 619 – 627. [PDFAbstract

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. 

Shen, S. V. (2019). Pricing Carbon to Contain Violence. The First International Research Conference on Carbon Pricing (peer-reviewed conference proceedings). Washington, D.C.: The World Bank Group.

Shao, Q., & Shen, S. V. (2017). When Reduced Working Time Harms the Environment: A Panel Threshold Analysis for EU-15, 1970-2010Journal 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. 

Other Publication

O’Brien, R. D., & Shen, S. V. (2013). The U.S., China, and Cybersecurity: The Ethical Underpinnings of a Controversial Geopolitical Issue.

Inactive Paper

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.