Assessment on the effect of pollution abatement on environmental efficiency with Markov chain Monte Carlo simulation

A. Ding, S. Managi

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Both environmental economists and policy makers have shown a great deal of interest in the effect of pollution abatement on environmental efficiency. In line with the modern resources available, however, no contribution is brought to the environmental economics field with the Markov chain Monte Carlo (MCMC) application, which enables simulation from a distribution of a Markov chain and simulating from the chain until it approaches equilibrium. The probability density functions gained prominence with the advantages over classical statistical methods in its simultaneous inference and incorporation of any prior information on all model parameters. This paper concentrated on this point with the application of MCMC to the database of China, the largest developing country with rapid economic growth and serious environmental pollution in recent years. The variables cover the economic output and pollution abatement cost from the year 1992 to 2003. We test the causal direction between pollution abatement cost and environmental efficiency with MCMC simulation. We found that the pollution abatement cost causes an increase in environmental efficiency through the algorithm application, which makes it conceivable that the environmental policy makers should make more substantial measures to reduce pollution in the near future.

本文言語英語
ホスト出版物のタイトルSustainable Development and Planning IV
出版社WITPress
ページ581-589
ページ数9
120
ISBN(印刷版)9781845641818
DOI
出版ステータス出版済み - 1 1 2009
外部発表はい
イベント4th International Conference on Sustainable Development and Planning, Sustainable Development 2009 - , キプロス
継続期間: 5 13 20095 15 2009

その他

その他4th International Conference on Sustainable Development and Planning, Sustainable Development 2009
Countryキプロス
Period5/13/095/15/09

All Science Journal Classification (ASJC) codes

  • Environmental Science(all)

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