Carbon import tariffs, traditionally considered a complement to domestic climate policy, are increasingly proposed as standalone policies. We build a quantitative trade model to compare U.S. carbon tariffs with and without a domestic carbon tax, each applied to a set of carbon-intensive, trade-exposed sectors. We find three main results. First, a U.S. carbon tariff increases U.S. emissions, lowers foreign emissions, and on net achieves half the global emissions reductions of the combined policy, which lowers both U.S. and foreign emissions. Second, both approaches increase U.S. GDP and welfare, but the combined policy has a larger effect due to terms of trade improvements. Third, global emissions reductions from multilateral tariff-only agreements are modest and do not increase monotonically with greater membership, whereas under combined policies they scale considerably with membership.

Global phenomena, such as climate change, often have local impacts that are spatially correlated. We show that greater spatial correlation of productivities can increase international inequality by increasing the correlation between a country's productivity and its gains from trade. We confirm this prediction using a half-century of exogenous variation in the spatial correlation of agricultural productivities induced by a global climatic phenomenon. We introduce this general-equilibrium effect into projections of climate-change impacts that typically omit spatial linkages and therefore do not account for the global scope of climate change. We project greater international inequality, with higher welfare losses across Africa.

Many behavioral responses to climate change are carbon-intensive, raising concerns that adaptation may cause additional warming. The sign and magnitude of this feedback depend on how increased emissions from cooling balance against reduced emissions from heating across space and time. We present an empirical approach that forecasts the effect of future adaptive energy use on global average temperature over the 21 st century. We estimate that energy-based adaptation will lower global mean surface temperature in 2099 by 0.07 to 0.12 °C relative to baseline projections under Representative Concentration Pathways 4.5 and 8.5. This cooling avoids 0.6 to 1.8 trillion U.S. Dollars ($2019) in damages, depending on the baseline emissions scenario. Energy-based adaptation lowers business-as-usual emissions for 85% of countries, reducing the mitigation required to meet their unilateral Nationally Determined Contributions by 20% on average. These findings indicate that while business-as-usual adaptive energy use is unlikely to accelerate warming, it raises important implications for countries’ existing mitigation commitments.

Across many domains, market-based interventions hold the promise of reducing costs through improved allocativeefficiencyinsettingswherepricesareotherwisemissing. Thisclaimisalsofundamentallychallenging toverify: theveryabsenceofpricesbeforeamarketmakesestablishingmisallocationchangesduetothemarket difficult. This paper develops an empirical framework showing how a theoretical change in allocative efficiency following a policy change can be recovered using a quasi-experimental panel data estimator, without needing inputprices. Weapplythisframework, togetherwithadministrativedata, tothestudyoftwomajorU.S.markets for air pollution, a canonical missing markets setting where concerns over high abatement costs have made market-based interventions particularly appealing. We find that for California’s RECLAIM program, where a pollution market replaced existing regulation, allocative efficiency improved by 10 percentage points. For the U.S.’s NO x Budget Program (NBP) in which a pollution market was overlaid onto existing regulation, we do not detect efficiency gains. Heterogeneity analyses suggest plants with pre-existing distortions in capital and labor, and facing restricted abatement options experienced lower allocative efficiency gains. While noisy, these findings shed light on the second-best conditions that may dampen the efficiency gains of pollution markets.

A global 2 °C climate target is projected to generate significant economic benefits. However, the presence of fossil fuel assets that are stranded as a consequence of climate change mitigation could complicate cost-benefit considerations at the country level. Here, we quantify the spatial distribution of stranded asset costs (SAC) together with that of the GDP benefits of climate mitigation (BCM). Under a 2 °C scenario, global total SAC is $19 trillion while global BCM is $63 trillion by 2050. At the country level, the sign of a country’s net benefit, the difference between BCM and SAC, is largely determined by the sign of its BCM. Net benefits are broadly positive across subtropical and tropical countries where high baseline temperatures imply GDP damage from climate change and negative across temperate countries where low baseline temperatures imply GDP gains. Notably, even major fossil fuel producers such as India, China, USA, and Saudi Arabia are projected to receive positive net benefits from a 2 °C scenario by 2050. Overall, 95% of global net benefit will be borne by low and lower-middle income countries. These results could inform the geopolitics of global climate change cooperation in the decades to come.

Compared to excise taxes and carbon taxes, setback restrictions on new oil wells have larger health benefits and worker compensation losses, but are more equitable by bringing greater benefits and lower losses to disadvantaged communities in California. For California to meet green gas emissions (GHG) targets, larger setbacks than currently proposed or additional supply-side policies are needed.

Market-based environmental policies are widely adopted on the basis of allocative efficiency. However, there is a growing distributional concern that market forces could increase the pollution exposure gap between disadvantaged and other communities by spatially reallocating pollution. We estimate how this “environmental justice gap” changed following the 2013 introduction of California’s carbon market, the world’s second largest and the one most subjected to environmental justice critiques. Embedding a pollution transport model within a program evaluation framework, we find that while the EJ gap was widening prior to 2013, it has since fallen by 21-30% across pollutants due to the policy.

Oil supply-side policies—setbacks, excise taxes and carbon taxes—are increasingly considered for decarbonizing the transportation sector. Understanding not only how such policies reduce oil extraction and greenhouse gas (GHG) emissions but also which communities receive the resulting health benefits and labour-market impacts is crucial for designing effective and equitable decarbonization pathways. Here we combine an empirical field-level oil-production model, an air pollution model and an employment model to characterize spatially explicit 2020–2045 decarbonization scenarios from various policies applied to California, a major oil producer with ambitious decarbonization goals. We find setbacks generate the largest avoided mortality benefits from reduced air pollution and the largest lost worker compensation, followed by excise and carbon taxes. Setbacks also yield the highest share of health benefits and the lowest share of lost worker compensation borne by disadvantaged communities. However, currently proposed setbacks may fail to meet California’s GHG targets, requiring either longer setbacks or additional supply-side policies.

Pollution concentrations (PM2.5) in the United States have fallen in recent decades. Despite these improvements, disparities in concentrations between racial/ethnic groups persist. We combine administrative data on the universe of emergency room (ER) admissions across California with satellite information on PM2.5 concentrations and compare recent trends in racial/ethnic disparities for PM2.5 and asthma rates. We find that PM2.5 concentrations fell for the average Black, Hispanic, and White individual. Similarly, disparities in PM2.5 concentrations fell between Black and White individuals and between Hispanic and White individuals. However, racial disparities in asthma rates, as measured by asthma-related ER visits per resident, have increased overall and broadly across the income distribution.

This paper quantifies and decomposes recent trends in US particulate matter (PM2.5) disparities from the electricity sector using a high-resolution pollution transport model. Between 2000 and 2018, PM2.5 concentrations from electricity fell by 89% for the average individual, more than double the decline rate in overall US ambient PM2.5 concentrations. Across racial/ethnic groups, we detect a dramatic convergence: since 2000, the Black-white PM2.5 disparity from electricity has narrowed by 95% and the Hispanic-white PM2.5 disparity has narrowed by 93%, though these disparities still exist in 2018. A decomposition reveals nearly all of these disparity trends can be attributed roughly equally to improvements in emissions intensities and compositional changes in electric generators, with small contributions from scale and residential location changes. This suggests both local air pollution policies and recent coal-to-natural gas fuel switching have played major roles in reducing US racial/ethnic pollution disparities from electricity. Although we detect similarly large PM2.5 improvements for the average low- and high-income individual, PM2.5 disparities by income are relatively small, with little change over time.

We appreciate Swartz et al. (1) for highlighting several key considerations for interpreting our results (2). While we discuss many of these in our paper, we are grateful to further highlight our work’s strengths, limitations, and future opportunities. A major challenge with understanding fisheries labor abuses is a lack of data. Automatic identification system (AIS) is only used by a subset of the global fishing fleet. However, AIS is valuable for monitoring certain types of fishing vessels, especially those that are large (∼52 to 85% carry AIS) (3) and those fishing on the high seas (∼80% carry AIS) (4). Mandating AIS and unique identifiers on fishing vessels and publishing vessel registries would facilitate more inclusive AIS-based analyses (5). Data on fisheries labor conditions are also limited. We spent over 1 y identifying public reports of forced labor onboard specific fishing vessels (“positives”). We also tried to identify a public list of specific fishing vessels free of forced labor (“negatives”) but were unable to, and therefore we were compelled to use positive-unlabeled learning. We assessed model performance using 10-fold cross-validation, an appropriate technique for small datasets (6) that uses resampling to train and validate multiple models using multiple training and separate validation data subsets. We estimated an average recall of 92%, the fraction of known positives correctly classified as positive (2). We used the term “high risk” for vessels classified as positive by the model for being above the threshold that maximizes a modified F1 score (7). While we cannot infer probability using this approach, it theoretically minimizes false positives and false negatives and equally weights the practical risks associated with both error types (7). Publishing information from forced labor vessel sanctions (5) (positives) and information from vessel inspections that identify either forced labor (positives) or decent working conditions (negatives) would increase training and testing data and facilitate more accurate analyses. Our analysis focused on prediction not causation. We did not estimate what causes forced labor but predicted whether vessels have forced labor using observable vessel features. While unpacking correlations between features would be critical in causal inference, understanding these correlations is less important for prediction. Nevertheless, we removed highly correlated model features during data preprocessing (2), which reduces model complexity while increasing feature importance interpretability (8). Moving forward, new research on causal relationships is critical, as are interventions that address causal drivers. When using predictive models, there is a risk that spurious or biased trends in the training data could lead to unjustified actions with serious human consequences (9). We recognize this ethical concern and stress the importance of further validation and evaluation of potential biases using new data. Nonetheless, predictive models can inform decisions within an otherwise opaque decision-making landscape (10). The path forward should include a suite of forced labor detection methods alongside interventions that address underlying drivers, reform labor policy, promote social responsibility in seafood production, and support victims. While we acknowledge the limitations of our approach, it lays the foundation for new opportunities to improve fisher working conditions.

Significance There is interest in whether COVID-19 cases respond to environmental conditions. If an effect is present, seasonal changes in local environmental conditions could alter the global spatial pattern of COVID-19 and inform local public health responses. Using a comprehensive global dataset of daily COVID-19 cases and local environmental conditions, we find that increased daily ultraviolet (UV) radiation lowers the cumulative daily growth rate of COVID-19 cases over the subsequent 2.5 wk. Although statistically significant, the implied influence of UV seasonality is modest relative to social distancing policies. Temperature and specific humidity cumulative effects are not statistically significant, and total COVID-19 seasonality remains to be established because of uncertainty in the net effects from seasonally varying environmental variables.

Environmental markets are widely prescribed as an alternative to open access regimes for natural resources. We develop a model of dynamic groundwater extraction to demonstrate how a spatial regression discontinuity design that exploits a spatially incomplete market for groundwater rights recovers a lower bound on the market’s net benefit. We apply this estimator to a major aquifer in water-scarce southern California and find that a groundwater market generated substantial net benefits, as capitalized in land values. Heterogeneity analyses point to gains arising in part from rights trading, enabling more efficient water use across sectors. Additional findings suggest that the market increased groundwater levels.

AbstractNearly every country is now combating the 2019 novel coronavirus (COVID-19). It has been hypothesized that if COVID-19 exhibits seasonality, changing temperatures in the coming months will shift transmission patterns around the world. Such projections, however, require an estimate of the relationship between COVID-19 and temperature at a global scale, and one that isolates the role of temperature from confounding factors, such as public health capacity. This paper provides the first plausibly causal estimates of the relationship between COVID-19 transmission and local temperature using a global sample comprising of 166,686 confirmed new COVID-19 cases from 134 countries from January 22, 2020 to March 15, 2020. We find robust statistical evidence that a 1°C increase in local temperature reduces transmission by 13% [−21%, −4%, 95%CI]. In contrast, we do not find that specific humidity or precipitation influence transmission. Our statistical approach separates effects of climate variation on COVID-19 transmission from other potentially correlated factors, such as differences in public health responses across countries and heterogeneous population densities. Using constructions of expected seasonal temperatures, we project that changing temperatures between March 2020 and July 2020 will cause COVID-19 transmission to fall by 43% on average for Northern Hemisphere countries and to rise by 71% on average for Southern Hemisphere countries. However, these patterns reverse as the boreal winter approaches, with seasonal temperatures in January 2021 increasing average COVID-19 transmission by 59% relative to March 2020 in northern countries and lowering transmission by 2% in southern countries. These findings suggest that Southern Hemisphere countries should expect greater transmission in the coming months. Moreover, Northern Hemisphere countries face a crucial window of opportunity: if contagion-containing policy interventions can dramatically reduce COVID-19 cases with the aid of the approaching warmer months, it may be possible to avoid a second wave of COVID-19 next winter.

While forced labor in the world's fishing fleet has been widely documented, its extent remains unknown. No methods previously existed for remotely identifying individual fishing vessels potentially engaged in these abuses on a global scale. By combining expertise from human rights practitioners and satellite vessel monitoring data, we show that vessels reported to use forced labor behave in systematically different ways from other vessels. We exploit this insight by using machine learning to identify high-risk vessels from among 16,000 industrial longliner, squid jigger, and trawler fishing vessels. Our model reveals that between 14% and 26% of vessels were high-risk, and also reveals patterns of where these vessels fished and which ports they visited. Between 57,000 and 100,000 individuals worked on these vessels, many of whom may have been forced labor victims. This information provides unprecedented opportunities for novel interventions to combat this humanitarian tragedy. More broadly, this research demonstrates a proof of concept for using remote sensing to detect forced labor abuses.

This upload contains all replication material for "Ultraviolet radiation decreases COVID-19 growth rates: Global causal estimates and seasonal implications" (preprint). Please note that this manuscript is under review and the data and code are likely to change (updated versions will be uploaded to Zenodo as soon as they are available). Authors: Tamma Carleton, Jules Cornetet, Peter Huybers, Kyle C. Meng, Jonathan Proctor. Code is located within CCHMP_covid_climate_code_release.zip, and is written in R, Stata, and Matlab. The working directory should be set to the repository folder at the top of each script. Please find the code needed to replicate the main findings of the paper described below: Plots of data: R and Stata scripts to make figures 1B, S1, S2, S3, S4 and S13 can be found within “code/analysis/data_plots/”. Regression analysis: Stata scripts to run the distributed lag regressions and plot the results in figures 2, S6, S7, S8 and S9 can be found within “code/analysis/regressions/” Seasonal simulations: R and Stata scripts to replicate the seasonal simulation shown in figures 3, S5 and S10 can be found within “code/analysis/seasonal_sim/”. SEIR simulations: Matlab scripts to replicate the SEIR simulations shown in figures S11 and S12 can be found within “code/analysis/SEIR/”. Data are located within CCHMP_covid_climate_data_release.zip.

Domestic political processes shape climate policy. In particular, there is increasing concern about the role of political lobbying over climate policy. This paper examines how lobbying spending on the Waxman–Markey bill, the most prominent and promising United States climate regulation so far, altered its likelihood of being implemented. We combine data from comprehensive United States lobbying records with an empirical method for forecasting the policy’s effect on the value of publicly listed firms. Our statistical analysis suggests that lobbying by firms expecting losses from the policy was more effective than lobbying by firms expecting gains. Interpreting this finding through a game-theoretic model, we calculate that lobbying lowered the probability of enacting the Waxman–Markey bill by 13 percentage points, representing an expected social cost of US$60 billion (in 2018 US dollars). Our findings also suggest how future climate policy proposals can be designed to be more robust to political opposition.

Randomized experiments have long been the gold standard in determining causal effects in ecological control–impact studies. However, it may be difficult to address many ecologically and policy‐relevant control–impact questions‐such as the effect of forest fragmentation or protected areas on biodiversity through experimental manipulation due to scale, costs and ethical considerations. Yet, ecologists may still draw causal insights in observational control–impact settings by exploiting research designs that approximate the experimental ideal. Here, we review the challenges of making causal inference in non‐experimental control–impact scenarios as well as a suite of statistical tools specifically designed to overcome such challenges. These tools are widely used in fields where experimental research is more limited (i.e., medicine, economics), and could be applied by ecologists across numerous sub‐disciplines. Using hypothetical examples, we discuss why bias is likely to plague observational control–impact studies in ways that do not surface with experimental manipulations, why bias is generally the barrier to causal inference, and different methods to overcome this bias. Satellite‐, survey‐ and citizen–science data hold great potential for advancing key questions in ecology that would otherwise be prohibitive to pursue experimentally. However, to harness such data to understand causal impacts of land, environmental and policy changes, we must expand our toolset such that we can improve inference and more confidently advance ecological understanding and science‐informed policy.