About

Maureen Cropper is a Distinguished University Professor with a background in economics, holding a B.A. from Bryn Mawr College (summa cum laude, 1969) and a Ph.D. from Cornell University (1973). She is a Senior Fellow at Resources for the Future and a former Lead Economist at the World Bank. Dr. Cropper has served as chair of the EPA Science Advisory Board Environmental Economics Advisory Committee and as past president of the Association of Environmental and Resource Economists. She is a member of the National Academy of Sciences and a Research Associate of the National Bureau of Economic Research. Her research focuses on valuing environmental amenities, especially environmental health effects, the discounting of future health benefits, and the tradeoffs implicit in environmental regulations. Her current work includes analyzing the costs and benefits of air pollution control in India, the health effects of plastics, and the benefits of urban transportation infrastructure. Throughout her career, she has contributed significantly to environmental and resource economics, emphasizing the economic valuation of environmental health impacts and policy analysis.

Research topics

• Environmental health
• Medicine
• Computer Science
• Economics
• Political Science
• Environmental science
• Environmental protection
• Neoclassical economics
• Meteorology
• Geography
• Economic growth
• Environmental planning
• Business
• Algorithm
• Law
• Natural resource economics

Selected publications

• Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine F -- Bright and low-redshift strong lenses

  arXiv (Cornell University) · 2026-03-30

  preprintOpen access

  We present 72 additional galaxy-galaxy strong lenses that complement the sample discovered in the Euclid Quick Release 1 data (63.1 deg^2) of the Strong Lens Discovery Engine (SLDE) papers A-E. It is shown that previous pre-selection of potential lenses, which excluded objects from the Gaia catalogue, led to missing several bright and low-redshift strong lenses, adding more than 10% new strong lens candidates compared to the previous search. In total, the catalogue includes 38 "grade A" (confident) and 34 "grade B" (probable) candidates. These lenses are identified through a combination of two independent searches for bright nearby objects: one based on machine-learning models followed by expert visual inspection, and the other based solely on expert visual inspection, targeting objects not included in the initial machine-learning selection (a limitation identified only after extensive visual inspection). With these additional strong lens candidates, we augment the expected number of high-confidence candidates in the Euclid Wide Survey from previous forecasts to 120000. Detailed semi-automated lens modelling confirms at least 41 systems out of 72, a fraction consistent with that found in SLDE A (315 out of 488). These include: multiple edge-on disc lenses; sources with arcs near the lens centre; "red sources"; and an edge-on disk galaxy lensing a galaxy merger, producing two sets of lensed features, an Einstein ring and a doubly imaged component. The median redshift of these systems is $Δ$ z ~ 0.3 lower than that of the SLDE A sample.

  PublisherDOI

• The Benefits of Public Transit to Households: Evidence from India

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Euclid preparation. Impact of redshift distribution uncertainties on the joint analysis of photometric galaxy clustering and weak gravitational lensing

  arXiv (Cornell University) · 2026-04-01

  preprintOpen access

  One of the $\textit{Euclid}$ mission's key projects is the so-called 3$\times$2pt analysis, that is, the combination of cosmic shear, photometric galaxy clustering, and galaxy-galaxy lensing. Although $\textit{Euclid}$ has established quality requirements for the photo-$z$ accuracy needed for the weak lensing galaxy sample, no such requirements have been set for the photometric clustering sample. In this paper, we investigate the impact of redshift uncertainties on $\textit{Euclid}$'s photometric galaxy clustering analysis and its combination with weak gravitational lensing, focusing on data release 1 (DR1). In particular, we study whether having precise knowledge of the mean of the redshift distributions per bin is sufficient to avoid biases in the resulting cosmological constraints or whether accuracy in the higher-order moments of the distribution is required. We evaluate the results based on their constraining power on $w_{\mathrm{0}}$ and $w_{a}$ and define thresholds for the precision and accuracy of $\textit{Euclid}$'s redshift distribution of the photometric clustering sample. We find that the redshift distributions of the photometric clustering sample must be known at an accuracy of 0.004(1+$z$) in the mean in order to recover 80$\%$ of the constraining power in $\textit{Euclid}$'s DR1 $w_{\mathrm{0}}w_{a}$CDM 3$\times$2pt analysis. The impact of the uncertainty on the width is negligible, provided the mean redshift is constrained with sufficient accuracy. For most sources of redshift distribution error, attaining the requirement on the mean will also reduce uncertainty in the width well below the required level.

  PublisherDOI

• Euclid Quick Data Release (Q1). From simulations to sky: Advancing machine-learning lens detection with real Euclid data

  Astronomy and Astrophysics · 2026-01-29

  articleOpen access

  In the era of large-scale surveys such as Euclid, machine learning has become an essential tool for identifying rare yet scientifically valuable objects, such as strong gravitational lenses. However, supervised machine-learning approaches require large quantities of labelled examples to train on, and the limited number of known strong lenses has led to a reliance on simulations for training. A well-known challenge is that machine-learning models trained on one data domain often underperform when applied to a different domain: in the context of lens finding, this means that strong performance on simulated lenses does not necessarily translate into equally good performance on real observations. 2 , 500 strong lens candidates were discovered through a synergy of machine learning, citizen science, and expert visual inspection. These discoveries now allow us to quantify this performance gap and investigate the impact of training on real data. We find that a network trained only on simulations recovers up to 92% of simulated lenses with 100% purity, but only achieves 50% completeness with 24% purity on real Euclid data. By augmenting training data with real Euclid lenses and non-lenses, completeness improves by 25--30% in terms of the expected yield of discoverable lenses in the Euclid Data Release 1 and the full Euclid Wide Survey. Roughly 20% of this improvement comes from the inclusion of real lenses in the training data, while 5--10% comes from exposure to a more diverse set of non-lenses and false positives from Q1. We show that the most effective lens-finding strategy for real-world performance combines the diversity of simulations with the fidelity of real lenses. This hybrid approach establishes a clear methodology for maximising lens discoveries in future data releases from Euclid and will likely also be applicable to other surveys such as the Vera Rubin Observatory's Legacy Survey of Space and Time.

  PublisherDOI

• Euclid Quick Data Release (Q1). AgileLens: A scalable CNN-based pipeline for strong gravitational lens identification

  arXiv (Cornell University) · 2026-04-08

  preprintOpen access

  We present an end-to-end, iterative pipeline for efficient identification of strong galaxy--galaxy lensing systems, applied to the Euclid Q1 imaging data. Starting from VIS catalogues, we reject point sources, apply a magnitude cut (I$_E$ $\leq$ 24) on deflectors, and run a pixel-level artefact/noise filter to build 96 $\times$ 96 pix cutouts; VIS+NISP colour composites are constructed with a VIS-anchored luminance scheme that preserves VIS morphology and NISP colour contrast. A VIS-only seed classifier supplies clear positives and typical impostors, from which we curate a morphology-balanced negative set and augment scarce positives. Among the six CNNs studied initially, a modified VGG16 (GlobalAveragePooling + 256/128 dense layers with the last nine layers trainable) performs best; the training set grows from 27 seed lenses (augmented to 1809) plus 2000 negatives to a colour dataset of 30,686 images. After three rounds of iterative fine-tuning, human grading of the top 4000 candidates ranked by the final model yields 441 Grade A/B candidate lensing systems, including 311 overlapping with the existing Q1 strong-lens catalogue, and 130 additional A/B candidates (9 As and 121 Bs) not previously reported. Independently, the model recovers 740 out of 905 (81.8%) candidate Q1 lenses within its top 20,000 predictions, considering off-centred samples. Candidates span I$_E$ $\simeq$ 17--24 AB mag (median 21.3 AB mag) and are redder in Y$_E$--H$_E$ than the parent population, consistent with massive early-type deflectors. Each training iteration required a week for a small team, and the approach easily scales to future Euclid releases; future work will calibrate the selection function via lens injection, extend recall through uncertainty-aware active learning, explore multi-scale or attention-based neural networks with fast post-hoc vetters that incorporate lens models into the classification.

  PublisherDOI

• Euclid: Galaxy morphology and photometry from bulge-disc decomposition of Early Release Observations

  ArXiv.org · 2025-02-21

  articleOpen access

  International audience

  PublisherOA PDF

• The Lancet Countdown on health and plastics

  The Lancet · 2025-08-03 · 75 citations

  review
  PublisherDOI

• Euclid Quick Data Release (Q1): First visual morphology catalogue

  ArXiv.org · 2025-03-19

  preprintOpen access

  International audience

  PublisherOA PDF

• Euclid Quick Data Release (Q1). The average far-infrared properties of Euclid-selected star-forming galaxies

  ArXiv.org · 2025-11-04

  articleOpen access

  The first Euclid Quick Data Release contains millions of galaxies with excellent optical and near-infrared (IR) coverage. To complement this dataset, we investigate the average far-IR properties of Euclid-selected main sequence (MS) galaxies using existing Herschel and SCUBA-2 data. We use 17.6deg$^2$ (2.4deg$^2$) of overlapping Herschel (SCUBA-2) data, containing 2.6 million (240000) MS galaxies. We bin the Euclid catalogue by stellar mass and photometric redshift and perform a stacking analysis following SimStack, which takes into account galaxy clustering and bin-to-bin correlations. We detect stacked far-IR flux densities across a significant fraction of the bins. We fit modified blackbody spectral energy distributions in each bin and derive mean dust temperatures, dust masses, and star-formation rates (SFRs). We find similar mean SFRs compared to the Euclid catalogue, and we show that the average dust-to-stellar mass ratios decreased from z$\simeq$1 to the present day. Average dust temperatures are largely independent of stellar mass and are well-described by the function $T_2+(T_1-T_2){\rm e}^{-t/τ}$, where $t$ is the age of the Universe, $T_1=79.7\pm7.4$K, $T_2=23.2\pm0.1$K, and $τ=1.6\pm0.1$Gyr. We argue that since the dust temperatures are converging to a non-zero value below $z=1$, the dust is now primarily heated by the existing cooler and older stellar population, as opposed to hot young stars in star-forming regions at higher redshift. We show that since the dust temperatures are independent of stellar mass, the correlation between dust temperature and SFR depends on stellar mass. Lastly, we estimate the contribution of the Euclid catalogue to the cosmic IR background (CIB), finding that it accounts for >60% of the CIB at 250, 350, and 500$μ$m. Forthcoming Euclid data will extend these results to higher redshifts, lower stellar masses, and recover more of the CIB.

  PublisherOA PDF

• Euclid Quick Data Release (Q1): Identification of massive galaxy candidates at the end of the Epoch of Reionisation

  ArXiv.org · 2025-11-14

  preprintOpen access

  Probing the presence and properties of massive galaxies at high redshift is one of the most critical tests for galaxy formation models. In this work, we search for galaxies with stellar masses M* > 10^10.25 Msun at z in [5,7], i.e., towards the end of the Epoch of Reionisation, over a total of ~23 deg^2 in two of the Euclid Quick Data Release (Q1) fields: the Euclid Deep Field North and Fornax (EDF-N and EDF-F). In addition to the Euclid photometry, we incorporate Spitzer Infrared Camera (IRAC) and ground-based optical data to perform spectral energy distribution (SED) fitting, obtaining photometric redshifts and derived physical parameters. After applying rigorous selection criteria, we identify a conservative sample of 145 candidate massive galaxies with M* > 10^10.25 Msun at z in [5,7], including 5 objects with M* > 10^11 Msun. This makes for a surface density of about 6.3 deg^-2 at z in [5,7], which should be considered a lower limit because of the current depth of the Euclid data (H_E < 24, 5 sigma in Q1). We find that the inferred stellar masses are consistent with galaxy formation models with standard star-formation efficiencies. These massive galaxies have colour excess E(B-V) values up to 0.75, indicating significant dust attenuation in some of them. In addition, half of the massive galaxies have best-fit ages comparable to the age of the Universe at those redshifts, which suggests that their progenitors were formed very early in cosmic time. About 78% of the massive galaxies lie on the star-forming main sequence (MS) in the SFR-M* plane, ~12% are found in the starburst region, and 10% in the transition zone between the MS and starbursts. We find no significant evidence for outshining or AGN contamination that could account for the elevated specific star-formation rates (sSFR) observed in the ~12% of galaxies classified as starbursts.

  PublisherOA PDFDOI

Frequent coauthors

• Joseph E. Aldy

  43 shared

• Paramita Sinha

  RTI International

  37 shared

• Martha Caulkins

  University of Maryland, College Park

  31 shared

• Anna Alberini

  30 shared

• Kabir Malik

  28 shared

• Maximilian Auffhammer

  University of California, Berkeley

  27 shared

• Richard D. Morgenstern

  Resources For The Future

  27 shared

• Arthur G. Fraas

  25 shared

Labs

• Maureen Cropper LabPI

Education

• B.A.

  Bryn Mawr College

  1969

• Ph.D.

  Cornell University

  1973

Awards & honors

• Senior Fellow at Resources for the Future
• Member of the National Academy of Sciences
• Research Associate of the National Bureau of Economic Resear…
• Past president of the Association of Environmental and Resou…