Submitted or in Revision

Shen, S. V. The Political Pollution Cycle. Abstract

How do incentives shape political behavior?  I show that local political leaders are incentivized to implement policies to varying degrees over time based on what they perceive to maximize the career payoffs for their efforts.  Studying the critical case of air pollution control policies in China, I advance a theory of the political pollution cycle to fathom the effect of political incentives on local policy implementation over time.  Using remote sensing, box modeling, observational data, and qualitative field research, I find that during 2000 and 2010, top prefectural leaders in China gradually ordered laxer regulation of pollution during their tenure so that the delivery of political achievements would bode well for their career advancement.  Such an effect is stronger when the leaders did not have connections with their political superiors.  Regulatory forbearance, rather than economic growth, explains the phenomenon, which unintentionally incurred tremendous human costs. 

Shen, S. V. Endogenizing the Costs of Climate-Induced Violence in the Optimal Management of the Climate: A MERGE Modeling Approach. [PDF] Abstract

Violence imposes an estimated annual total cost equivalent to 11 percent of the world’s GDP.  However, violence has rarely appeared in economic models partly because it is exceptionally challenging to do.  In the meantime, scientific evidence points to an active link between climate change and the incidence of interpersonal and inter-group violence.  This paper connects the climate-economy and the climate-violence systems by putting forth a new method to endogenize the costs of climate-induced violence in the optimal management of the climate.  Using the established MERGE integrated assessment model, I find that based on the median estimates of the climate-violence relationship, such internalization can roughly double the optimal carbon price consistently over time in most sensitivity scenarios.  To further account for the uncertainty around the magnitude of violence damage as a result of temperature rise, I find that based on high-bound estimates, the optimal carbon prices are projected to rise to between six and eight times those in the business-as-usual scenario.  Normatively, sub-Saharan Africa is estimated to avoid damages related to climate-induced violence that is worth 1 percent of the regional GDP in 2050, 9 percent in 2100, and 27 percent in 2200.  These 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.

Peer-Reviewed Publications

Kopas, J., York, E., Jin, X., Harish, S. P., Kennedy, R., Shen, S. V., & Urpelainen, J. (2020). Environmental Justice in India: Incidence of Air Pollution from Coal-Fired Power Plants. Ecological Economics, 176, 106711. [PDFAbstract

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. 

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. (2019). Pricing Carbon to Contain Violence. In The First International Research Conference on Carbon Pricing (pp. 331–348). Washington, D.C.: World Bank.

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. 

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. [PDF] 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.