The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Environmental production functions and environmental directional distance functions
Abstract
This study derives the relationship between environmental production functions and environmental directional distance functions. These two approaches make different assumptions when modeling the joint production of good and bad outputs. The environmental production function credits a producer solely for expanding good output production, while the directional environmental distance function credits a producer for simultaneously increasing production of the good output and reducing production of bad outputs. Estimates of technical efficiency and pollution abatement costs are calculated using data from coal-fired power plants. These results provide the empirical basis for comparing the environmental production function to the environmental directional distance function.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Toward a New Conception of the Environment-Competitiveness Relationship
Porter, M. E., & Linde, C. V. D. (1995) · Journal of Economic Perspectives · doi:10.1257/jep.9.4.97 · Cited by 13,365
Cited by
Share green growth: regional evaluation of green output performance in China
Song, M., Zhu, S., Wang, J., & Zhao, J. (2020) · International Journal of Production Economics · doi:10.1016/j.ijpe.2019.05.012 · Cited by 156
Liu, Y., Liu, M., Wang, G., Zhao, L., & An, P. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-13780-2 · Cited by 120
Cui, Q., Wei, Y.-M., & Li, Y. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.09.048 · Cited by 81
Metadata
- Authors
- FARE, R., GROSSKOPF, S., & PASURKAJR, C.
- Venue
- Energy
- Publisher
- Elsevier BV
- Published
- 2007-07
- Volume
- 32
- Issue
- 7
- Pages
- 1055-1066
- ISSN
- 0360-5442 (ISSN-L)
- OpenAlex
- W2023262539
- Cited by
- 807
- References
- 1
- FWCI
- 2.88
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Environmental production functions and environmental directional distance functions. https://app.cochat.ai/papers/10.1016/j.energy.2006.09.005
- The paper
- FARE, R., GROSSKOPF, S., & PASURKAJR, C. (2007). Environmental production functions and environmental directional distance functions. Energy. https://doi.org/10.1016/j.energy.2006.09.005
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
