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Cited by 491

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Non‐Mandatory Approaches to Environmental Protection

Khanna, Madhu·2001·Journal of Economic Surveys

Abstract

The approach to environmental protection has been evolving from a regulation‐driven, adversarial ‘government‐push’ approach to a more proactive approach involving voluntary and often ‘business‐led’ initiatives to self‐regulate their environmental performance. This has been accompanied by increasing provision of environmental information about firms and products to enlist market forces and communities in creating a demand for corporate environmental self‐regulation by signaling their preferences for environmentally friendly firms. This paper provides an overview of the non‐mandatory approaches being used for environmental protection and surveys the existing theoretical literature analyzing the economic efficiency of such approaches relative to mandatory approaches. It also discusses empirical findings on the factors motivating self‐regulation by firms and its implications for their economic and environmental performance. It examines the existing evidence on the extent to which information disclosure is effective in generating pressures from investors and communities on firms to improve their environmental performance.

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Metadata

Authors
Khanna, M.
Venue
Journal of Economic Surveys
Publisher
Wiley
Published
2001-07
Volume
15
Issue
3
Pages
291-324
ISSN
  • 0950-0804 (ISSN-L)
  • 1467-6419
OpenAlex
W2004616145
Cited by
491
References
2
FWCI
58.68
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Non‐Mandatory Approaches to Environmental Protection. https://app.cochat.ai/papers/10.1111/1467-6419.00141
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Khanna, M. (2001). Non‐Mandatory Approaches to Environmental Protection. Journal of Economic Surveys. https://doi.org/10.1111/1467-6419.00141

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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.