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Measuring the Environmental Efficiency and Technology Gap of PM(2.5) in China’s Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model

Since air pollution is an important factor hindering China’s economic development, China has passed a series of bills to control air pollution. However, we still lack an understanding of the status of environmental efficiency in regard to air pollution, especially PM(2.5) (diameter of fine particula...

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Autores principales: Cheng, Shixiong, Xie, Jiahui, Xiao, De, Zhang, Yun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6406289/
https://www.ncbi.nlm.nih.gov/pubmed/30823601
http://dx.doi.org/10.3390/ijerph16040675
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author Cheng, Shixiong
Xie, Jiahui
Xiao, De
Zhang, Yun
author_facet Cheng, Shixiong
Xie, Jiahui
Xiao, De
Zhang, Yun
author_sort Cheng, Shixiong
collection PubMed
description Since air pollution is an important factor hindering China’s economic development, China has passed a series of bills to control air pollution. However, we still lack an understanding of the status of environmental efficiency in regard to air pollution, especially PM(2.5) (diameter of fine particulate matter less than 2.5 μm) pollution. Using panel data on ten major Chinese city groups from 2004 to 2016, we first estimate the environmental efficiency of PM(2.5) by epsilon-based measure (EBM) meta-frontier model. The results show that there are large differences in PM(2.5) environmental efficiency between cities and city groups. The cities with the highest environmental efficiency are the most economically developed cities and the city group with the highest environmental efficiency is mainly the eastern city group. Then, we use the meta-frontier Malmquist EBM model to measure the meta-frontier Malmquist total factor productivity index (MMPI) in each city group. The results indicate that, overall, China’s environmental total factor productivity declined by 3.68% and 3.49% when considering or not the influence of outside sources, respectively. Finally, we decompose the MMPI into four indexes, namely, the efficiency change (EC) index, the best practice gap change (BPC) index, the pure technological catch-up (PTCU) index, and the frontier catch-up (FCU) index. We find that the trend of the MMPI is consistent with those of the BPC and PTCU indexes, which indicates that the innovation effect of the BPC and PTCU indexes are the main driving forces for productivity growth. The EC and FCU effect are the main forces hindering productivity growth.
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spelling pubmed-64062892019-03-21 Measuring the Environmental Efficiency and Technology Gap of PM(2.5) in China’s Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model Cheng, Shixiong Xie, Jiahui Xiao, De Zhang, Yun Int J Environ Res Public Health Article Since air pollution is an important factor hindering China’s economic development, China has passed a series of bills to control air pollution. However, we still lack an understanding of the status of environmental efficiency in regard to air pollution, especially PM(2.5) (diameter of fine particulate matter less than 2.5 μm) pollution. Using panel data on ten major Chinese city groups from 2004 to 2016, we first estimate the environmental efficiency of PM(2.5) by epsilon-based measure (EBM) meta-frontier model. The results show that there are large differences in PM(2.5) environmental efficiency between cities and city groups. The cities with the highest environmental efficiency are the most economically developed cities and the city group with the highest environmental efficiency is mainly the eastern city group. Then, we use the meta-frontier Malmquist EBM model to measure the meta-frontier Malmquist total factor productivity index (MMPI) in each city group. The results indicate that, overall, China’s environmental total factor productivity declined by 3.68% and 3.49% when considering or not the influence of outside sources, respectively. Finally, we decompose the MMPI into four indexes, namely, the efficiency change (EC) index, the best practice gap change (BPC) index, the pure technological catch-up (PTCU) index, and the frontier catch-up (FCU) index. We find that the trend of the MMPI is consistent with those of the BPC and PTCU indexes, which indicates that the innovation effect of the BPC and PTCU indexes are the main driving forces for productivity growth. The EC and FCU effect are the main forces hindering productivity growth. MDPI 2019-02-25 2019-02 /pmc/articles/PMC6406289/ /pubmed/30823601 http://dx.doi.org/10.3390/ijerph16040675 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cheng, Shixiong
Xie, Jiahui
Xiao, De
Zhang, Yun
Measuring the Environmental Efficiency and Technology Gap of PM(2.5) in China’s Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model
title Measuring the Environmental Efficiency and Technology Gap of PM(2.5) in China’s Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model
title_full Measuring the Environmental Efficiency and Technology Gap of PM(2.5) in China’s Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model
title_fullStr Measuring the Environmental Efficiency and Technology Gap of PM(2.5) in China’s Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model
title_full_unstemmed Measuring the Environmental Efficiency and Technology Gap of PM(2.5) in China’s Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model
title_short Measuring the Environmental Efficiency and Technology Gap of PM(2.5) in China’s Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model
title_sort measuring the environmental efficiency and technology gap of pm(2.5) in china’s ten city groups: an empirical analysis using the ebm meta-frontier model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6406289/
https://www.ncbi.nlm.nih.gov/pubmed/30823601
http://dx.doi.org/10.3390/ijerph16040675
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