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Does migration of pollution-intensive industries impact environmental efficiency? Evidence supporting "Pollution Haven Hypothesis".

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The existence of Pollution Haven Hypothesis (PHH) is still being hot debated due to its importance in environmental and industrial policymaking. However, research concerning the PHH in relation to local… Click to show full abstract

The existence of Pollution Haven Hypothesis (PHH) is still being hot debated due to its importance in environmental and industrial policymaking. However, research concerning the PHH in relation to local areas and regions within country borders is limited. Therefore, this research focused on the relationship between the migration of pollution-intensive industries (PIIs) and environmental efficiency (EE) at the prefecture level throughout China's Guangdong Province from 2001 to 2014. Firstly, this research found that many PIIs migrated from the core industrial region of the Pearl River Delta (PRD) to peripheral Non-Pearl River Delta (NPRD) areas after 2006. A Data Envelopment Analysis (DEA) model was used to evaluate industrial EE of a range of different cities. Then, analysis using PMG/ARDL regression model shows that industrial EE maintained a negative long run equilibrium relationship with PII migration in the NPRD region where PIIs have moved out, but had little correlation with PII migration in the PRD where PIIs have moved in. The results show pollution transfer caused by migration of PIIs from the PRD to the NPRD region in Guangdong Province that supports the PHH. The NPRD had become a pollution haven for PIIs in the PRD. This study proposes that policymakers should build a series of environmental regulatory policies and industrial developing policies to avoid the creation of pollution havens in the developing area in China, instead of simply pollution emission control policies.

Keywords: pollution intensive; pollution; pollution hypothesis; intensive industries; migration; migration pollution

Journal Title: Journal of environmental management
Year Published: 2019

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