Open AccessCan embedded knowledge in pollution prevention techniques reduce greenhouse gas emissions? A case of the power generating industry in the United States
Author(s) -
Sangyoul Lee,
Xiang Bi
Publication year - 2020
Publication title -
environmental research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.37
H-Index - 124
ISSN - 1748-9326
DOI - 10.1088/1748-9326/abc589
Subject(s) - greenhouse gas , leverage (statistics) , pollution , pollutant , environmental science , business , environmental economics , knowledge transfer , pollution prevention , natural resource economics , environmental resource management , economics , engineering , computer science , waste management , ecology , chemistry , management , organic chemistry , machine learning , biology
Following the well-known public information disclosure program, the Toxics Release Inventory (TRI), the United States established the Greenhouse Gas Reporting Program (GHGRP), which documents annual direct GHG emissions from major point-source polluters from 2010 onwards. While recorded GHG emissions in the GHGRP have declined over time, few studies have shed light on the mechanism through which such reduction is achieved. This paper empirically examines whether experience in managing toxic pollutants subject to the TRI pre-GHGRP contributed to the decrease in GHG emissions post-GHGRP. We use data from electrical power plants to construct various measures for the magnitude and diversity of knowledge in pollution prevention (P2) pre-GHGRP. Using a difference-in-differences framework with first-differenced panel data, we find that electrical power plants with abundant experience in P2 achieved a greater reduction in GHG emissions by 2.3%–4.8% compared to less-experienced plants post-GHGRP. This suggests that policymakers can leverage a plant’s prior knowledge and experience with P2 techniques to develop targeted strategies to facilitate the transfer of embedded knowledge to other firms and pollutants.