Journal of Environmental Economics and Management

The files contain the replication data and replication instructions (as STATA files) that replicate the results presented in the article: Brunnschweiler, Christa and Steven Poelhekke (2021), Pushing One's Luck: Petroleum ownership and discoveries, Journal of Environmental Economics and Management 109: 102506. doi: 10.1016/j.jeem.2021.102506 The files also allow replication of the results in the Online Appendix linked to this paper.

Readme file describing all data sources and analysis files used in "Superfund Cleanups and Children’s Lead Exposure"

This is the data and do file for the first round of review. We will gladly provide updated data and code if the journal accepts our article. The data and code will also be posted on the Colorado State University website.

This zip file contains a technical appendix ("Trade_and_Enviro_Numerical_Appendix.pdf") that describes the parameter values and system of equations contained in the included Matlab files. The zip file also contains all of the Matlab files used to solve and simulate the combined framework in "THE ENVIRONMENTAL EFFECTS OF TRADE WITHIN AND ACROSS SECTORS." All of the data used to generate the figures and tables in the paper were generated using these files and the included Excel sheets..

This data package contains all raw data and codes used for the research paper. It contains six files, including an excel file, a Stata do-file, and 4 .dta data files. Readers need to have Stata and MS Excel to reproduce the analysis. See the do-file for more details.

Lab experiment data and analysis in Stata for paper published in JEEM

My data set pools different sources of information: information on plant-level TRI emissions, newspapers location, newspapers content, and demographics measured in 2-km rings around the TRI plants. Please find details about each source in Section III of the paper. The different sources are pooled together in the dataset named ``dataset press and leaks.dta" The dataset named ``articles distance.dta" contains information on plants that have at least one newspaper within 90 km from their location, observed between 1996 and 2009. It reports their respective distance from each of these newspapers. The dataset also records whether the plant's TRI emissions are covered or not in each of these newspapers every year. See Section III in the paper for more details. This dataset is needed to reproduce Figure 1. Do-files that reproduce all the Figures and Tables in the paper are included in the folder. If you have any questions do not hesitate to contact me.

Four files are attached : - OptimalThreshold_JEEM_R1_data.Rdata (Rdata format) contains the simulation results (emissions, gross margin, agricultural area, livestock units) at the representative farm level (see Section 4) for an emission tax varying from 0 to 200 EUR/tCO2eq, as well as the data retrieved from Bellassen et al (2015) about MRV costs; - OptimalThreshold_JEEM_R1_data.r contains the R code necessary for the design of the various scenarios explored in the paper (emission tax level, magnitude and distribution of MRV costs, choice of the criterion) and determine the respective optimal coverage; - OptimalThreshold_JEEM_R1_tables.r contains the R code for the tables presented in the paper; - OptimalThreshold_JEEM_R1_graphes.r contains the R code used to construct the figures presented in the paper.

The zip folder contains the model codes and data used to reproduce the results for Section 4 in the paper.

These files contain the data and data dictionary used to produce the results presented in: Moeltner, K., Puri, R., Johnston, R. J., Besedin, E., Balukas, J. A., & Le, A. (2023). Locally-weighted meta-regression and benefit transfer. Journal of Environmental Economics and Management, 121, 102871. Meta-regression models (MRMs) are commonly used within benefit transfer to estimate broadly applicable, “umbrella” benefit functions that may be used to predict willingness to pay for environmental quality improvements at sites for which primary valuation studies have not been conducted. In virtually all benefit transfers of this type, a single regression model is fit to all source points in the metadata, and used to produce out-of-sample predictions for all possible policy-site applications. Despite the advantages of this approach over other types of benefit transfer, the predictive accuracy of these MRMs generally leaves room for improvement. This dataset enables reproduction of the presented locally-weighted regression approach to MRM estimation, for an empirical application on willingness-to-pay for water quality improvements. The metadata are drawn from primary stated preference studies that estimate per household (use and nonuse) WTP for water quality changes in specific U.S. water bodies. Changes in water quality, in turn, affect ecosystem services including aquatic life support, recreational uses (such as fishing, boating, and swimming), and nonuse values. Studies were limited to those for which WTP estimates could be readily mapped to water quality changes measured on a standard 100-point Water Quality Index (WQI). All monetary values were adjusted to 2019 U.S. dollars. The data includes 188 observations from 58 prior stated preference studies, with the earliest of these published in 1980. Variable definitions are provided in the attached data dictionary file. Additional details of the metadata are described in Moeltner et al. (2023).