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Intermittent monitoring of environmental standards may induce strategic changes in polluting activities. This paper documents local strategic responses to a cyclical, once-every-six-day air quality monitoring schedule under the federal Clean Air Act. Using satellite data of monitored areas, I show that air quality is significantly worse on unmonitored days. This effect is explained by short-term suppression of pollution on monitored days, especially during high-pollution periods when the city’s noncompliance risk is high. Cities’ use of air quality warnings increases on monitored days, which suggests local governments’ role in coordinating emission reductions. (JEL K32, Q35, Q58, R11)

Significance Understanding the drivers of abundance and biodiversity decline across numerous taxa is imperative for designing conservation policy. We use highly detailed citizen science data to show that there is a strong, robust negative association between bird abundance and ambient ozone concentrations in the United States. In particular, we find that a regulation aimed at reducing ozone precursors has significantly bolstered populations in the eastern United States. Our estimated effects suggest that the large decline in average US ozone concentrations over the past several decades has averted the loss of potentially billions of birds. Environmental policies nominally aimed at humans can also provide substantial benefits to other species.

Species extinctions and ecological degradation are accelerating to a degree unprecedented in human history. Despite such trends, causal evidence for economic drivers of biodiversity loss and effective policy responses remains sparse. Here we study the relationship between economic production and biodiversity using a novel panel dataset that contains detailed and consistently reported information on the types and quantities of wildlife at thousands of locations across the United States between 1960 and 2015. Our research design exploits well- understood sources of change to local economic output – including those induced by fiscal shocks and environmental regulations – to identify how local economic production affects biodiversity outcomes. We find that economic production re- duces the total abundance of wildlife, reduces the count of distinct species, and changes the composition of species in a local ecosystem even holding the number of species constant. Our findings point toward environmental degradation as a potential culprit in the decline of biodiversity. We show that the adverse effect of economic production is mitigated by conservation, and by advances in emission abatement technologies that were spurred by stricter pollution regulations.

Regulators often rely on regulated entities to self-monitor compliance, potentially creating strategic incentives for endogenous monitoring. This paper builds a framework to detect whether local governments skip air pollution monitoring when they expect air quality to deteriorate. The core of our method tests whether the timing of monitor shutdowns coincides with the counties’ air quality alerts – public advisories based on local governments’ own pollution forecasts. Applying the method to a monitor in Jersey City, NJ, suspected of a deliberate shutdown during the 2013 “Bridgegate” traffic jam, we find a 33% reduction of this monitor’s sampling rate on pollution-alert days. Building on large-scale inference tools, we then apply the method to test more than 1,300 monitors across the U.S., finding 14 metro areas with clusters of monitors showing similar strategic behavior. We assess geometric imputation and remote-sensing technologies as potential solutions to deter future strategic monitoring.

Many human activities can be strategically timed around forecastable natural hazards to mute their impacts. We study air pollution shock mitigation in a high-stakes healthcare setting: hospital surgery scheduling. Using newly available inpatient surgery records from a major city in China, we track post-surgery survival for over 1 million patients, and document a significant increase of hospital mortality among those who underwent surgeries on days with high particulate matter pollution. This effect has two special features. First, pollution on the surgery day, rather than exposure prior to hospitalization, before or after the surgery, is primarily explanatory of the excess mortality. Second, a small but high-risk group – elderly patients undergoing respiratory or cancer operations – bears a majority of pollution’s damages. Based on these empirical findings, we build and analyze a model of hospital surgery scheduling. For over a third of the high-risk surgeries, there exists an alternative, lower-pollution day within three days such that moving the surgery may lead to a Pareto improvement in survival.

The decade from 2010 to 2019 marked a significant turning point in China’s approach to environmental regulation and pollution. This article describes the recent trends in air and water qual...

The decade from 2010 to 2019 marked a significant turning point in China’s history of environmental regulation and pollution. This article describes the recent trends in air and water quality, with a focus on the five years since China declared a “war on pollution” in 2014. It summarizes the emerging literature that has taken advantage of accompanying improvements in data availability and accuracy to document sharp improvements in environmental quality, especially local air pollution, and understand their social, economic, and health consequences. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

We study a new consumption stimulus model that leverages mobile payment platforms to dispense massive amounts of small-value, use-it-this-week-or-lose-it digital coupons. We evaluate the effects of one such program in a large Chinese city using novel data of mobile platform transactions of 1 million program participants. Exploiting participants’ rush to the first-come, first-served digital portal, we compare spending among those who barely won coupons to those who barely lost because of minor differences in the timing of their arrival at the portal. We find that coupons generate an immediate increase in weekly consumption among winners by $3 additional out-of-pocket spending for every $1 in government subsidy – representing an order-of-magnitude improvement in fiscal cost-benefit relative to a traditional cash-based stimulus. Analysis of business customer flows suggests that coupons distort consumption toward more expensive options, leading the program to disproportionately favor big firms that sell pricier goods and services. Relaxing coupons’ minimum spending requirements would alleviate such distributional concern without sacrificing consumer welfare. We conclude that the coupon model can be a useful addition to policy makers’ stimulus toolbox.

Neural network quantum states provide a novel representation of the many-body states of interacting quantum systems and open up a promising route to solve frustrated quantum spin models that evade other numerical approaches. Yet its capacity to describe complex magnetic orders with large unit cells has not been demonstrated, and its performance in a rugged energy landscape has been questioned. Here we apply restricted Boltzmann machines and stochastic gradient descent to seek the ground states of a compass spin model on the honeycomb lattice, which unifies the Kitaev model, Ising model and the quantum 120$^\circ$ model with a single tuning parameter. We report calculation results on the variational energy, order parameters and correlation functions. The phase diagram obtained is in good agreement with the predictions of tensor network ansatz, demonstrating the capacity of restricted Boltzmann machines in learning the ground states of frustrated quantum spin Hamiltonians. The limitations of the calculation are discussed. A few strategies are outlined to address some of the challenges in machine learning frustrated quantum magnets., published version

Endometriosis implants are comprised of glandular and stromal elements, macrophages, nerves, and blood vessels and are commonly accompanied by pelvic pain. We propose that activated macrophages are recruited to and infiltrate nascent lesions, where they secrete proinflammatory cytokines, promoting the production of chemokines, neurotrophins, and angiogenic growth factors that sustain an inflammatory microenvironment. Immunohistochemical evaluation of endometriosis lesions reveals in situ colocalization of concentrated macrophages, brain-derived neurotrophic factor (BDNF), and nerve fibers. These observations were coupled with biochemical analyses of primary eutopic endometriosis stromal cell (EESC) cultures, which allowed defining potential pathways leading to the neuroangiogenic phenotype of these lesions. Our findings indicate that IL-1β potently (EC50 = 7 ± 2 ng/mL) stimulates production of EESC BDNF at the mRNA and protein levels in an IL-1 receptor-dependent fashion. Selective kinase inhibitors demonstrate that this IL-1β effect is mediated by c-Jun N-terminal kinase (JNK), NF-κB, and mechanistic target of rapamycin signal transduction pathways. IL-1β regulation of regulated on activation normal T cell expressed and secreted (RANTES), a prominent EESC chemokine, also relies on JNK and NF-κB. An important clinical implication of the study is that interference with BDNF and RANTES production, by selectively targeting the JNK and NF-κB cascades, may offer a tractable therapeutic strategy to mitigate the pain and inflammation associated with endometriosis.

This cross-sectional study examines the association between thunderstorm-related atmospheric changes and emergency department visits for acute respiratory illnesses among Medicare beneficiaries.