File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Stakeholder concerns of air pollution in Hong Kong and policy implications: A big-data computational text analysis approach

TitleStakeholder concerns of air pollution in Hong Kong and policy implications: A big-data computational text analysis approach
Authors
KeywordsAir pollution
Big data
Computational text analysis
Stakeholder concerns
Public health
Issue Date2019
PublisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/envsci
Citation
Environmental Science & Policy, 2019, v. 101, p. 374-382 How to Cite?
AbstractStakeholder engagement is critical to the successful formulation and implementation of environmental policies. Text analysis can serve as an important means to decipher stakeholder (dis)agreements. Traditionally, researchers analyze texts using qualitative text analysis. Relying only on qualitative text analysis can no longer be sufficient to handle very large amounts of textual data. Now computational methods can be applied to help extract opinions in electronic texts. Our paper adopts a big data computational text analysis approach (keyword analysis, keyword co-occurrence, thematic analysis), based on a corpus of 2.4 million words, to compare concerns towards air pollution among three stakeholder groups (i.e. the government (GOV), the environmental groups (NGO) and the news media (MEDIA)) in Hong Kong, between 2002 and 2012, a period when air pollution was subject to rigorous policy debates and discussions. Our analysis shows the somewhat different concerns of the stakeholders. The stakeholders focus heavily on emissions and end-of-pipe pollution control. Though the advocation of sustainability since 1990s, the government-led command-and-control approach still dictates the discourse. Sustainability is featured more intensively by the Hong Kong Special Administrative Region (HKSAR) Government. Transboundary pollution (e.g. from Guangdong, China) is not a major concern for all stakeholder groups. Public health is a greater concern in MEDIA, as compared to the other two stakeholder groups. Future air pollution policy-making may direct more of its attention to the pressing concerns over the negative impacts of air pollution on health and its associated costs, with health experts getting more closely involved in the regulatory decision-makings. More information should be given to vulnerable groups, such as young children or workers in polluted environments. To better relate differential health risks of pollution in everyday life, both local and international governments should consider developing more personalized air pollution monitoring and health management systems for their citizens in future.
Persistent Identifierhttp://hdl.handle.net/10722/287617
ISSN
2019 Impact Factor: 4.767
2015 SCImago Journal Rankings: 1.656

 

DC FieldValueLanguage
dc.contributor.authorLam, JCK-
dc.contributor.authorCheung, LYL-
dc.contributor.authorWang, S-
dc.contributor.authorLi, VOK-
dc.date.accessioned2020-10-05T12:00:43Z-
dc.date.available2020-10-05T12:00:43Z-
dc.date.issued2019-
dc.identifier.citationEnvironmental Science & Policy, 2019, v. 101, p. 374-382-
dc.identifier.issn1462-9011-
dc.identifier.urihttp://hdl.handle.net/10722/287617-
dc.description.abstractStakeholder engagement is critical to the successful formulation and implementation of environmental policies. Text analysis can serve as an important means to decipher stakeholder (dis)agreements. Traditionally, researchers analyze texts using qualitative text analysis. Relying only on qualitative text analysis can no longer be sufficient to handle very large amounts of textual data. Now computational methods can be applied to help extract opinions in electronic texts. Our paper adopts a big data computational text analysis approach (keyword analysis, keyword co-occurrence, thematic analysis), based on a corpus of 2.4 million words, to compare concerns towards air pollution among three stakeholder groups (i.e. the government (GOV), the environmental groups (NGO) and the news media (MEDIA)) in Hong Kong, between 2002 and 2012, a period when air pollution was subject to rigorous policy debates and discussions. Our analysis shows the somewhat different concerns of the stakeholders. The stakeholders focus heavily on emissions and end-of-pipe pollution control. Though the advocation of sustainability since 1990s, the government-led command-and-control approach still dictates the discourse. Sustainability is featured more intensively by the Hong Kong Special Administrative Region (HKSAR) Government. Transboundary pollution (e.g. from Guangdong, China) is not a major concern for all stakeholder groups. Public health is a greater concern in MEDIA, as compared to the other two stakeholder groups. Future air pollution policy-making may direct more of its attention to the pressing concerns over the negative impacts of air pollution on health and its associated costs, with health experts getting more closely involved in the regulatory decision-makings. More information should be given to vulnerable groups, such as young children or workers in polluted environments. To better relate differential health risks of pollution in everyday life, both local and international governments should consider developing more personalized air pollution monitoring and health management systems for their citizens in future.-
dc.languageeng-
dc.publisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/envsci-
dc.relation.ispartofEnvironmental Science & Policy-
dc.subjectAir pollution-
dc.subjectBig data-
dc.subjectComputational text analysis-
dc.subjectStakeholder concerns-
dc.subjectPublic health-
dc.titleStakeholder concerns of air pollution in Hong Kong and policy implications: A big-data computational text analysis approach-
dc.typeArticle-
dc.identifier.emailLam, JCK: h9992013@hkucc.hku.hk-
dc.identifier.emailLi, VOK: vli@eee.hku.hk-
dc.identifier.authorityLam, JCK=rp00864-
dc.identifier.authorityLi, VOK=rp00150-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.envsci.2019.07.007-
dc.identifier.scopuseid_2-s2.0-85071300764-
dc.identifier.hkuros315133-
dc.identifier.volume101-
dc.identifier.spage374-
dc.identifier.epage382-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats