Assessment On The Effect Of Pollution Abatement
On Environmental Efficiency With
Markov Chain Monte Carlo Simulation
Author(s)
A. Ding & S. Managi
Abstract
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.
Keywords: environmental efficiency, pollution abatement, Markov chain Monte
Carlo, China, environmental productivity, environmental performance.
Keywords
environmental efficiency, pollution abatement, Markov chain Monte
Carlo, China, environmental productivity, environmental performance.
Related Book
Other papers in this volume
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