Spatial Effects of Participation in Global Value Chains on CO₂ Emissions: A Global Spillover Perspective

ABSTRACT

Using balanced panel data of 62 countries (regions) from 1995 to 2011, we explore the linkage between a country’s participation in global value chains (GVC) and its carbon emissions using spatial panel econometric models. We find: First, positive spatial dependency does exist between countries. Second, forward and backward GVC participation has different spatial spillover effects, with the latter causing most of the spillovers. Third, regarding industry heterogeneity, the manufacturing sector generates a stronger spatial spillover than the service sector. High-tech manufacturing sub-industries show stronger spillovers, compared to low-tech sub-industries. Finally, we propose policy suggestions for international relations and environmental governance.

KEYWORDS:

• Global value chain (GVC)
• CO₂ emissions
• spatial econometric model

Acknowledgments

We would like to thank the Editor-in-Chief Professor Paresh Kumar Narayan and two anonymous referees for helpful comments and suggestions which improved the quality of the article immensely.

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website.

Notes

1. If environmental pollution events occur frequently in one country, the neighboring regions of this country may be affected owing to currents and wind direction.

2. Industrial agglomeration refers to the clustering of numerous firms in a related area, e.g., Southeast Asia Manufacturing Center, China’s coastal area.

3. These chains usually cover the high value-added segments such as the research and design of chip, brands operation, and sales.

4. For example, Southeast Asia Manufacturing Center; China’s coastal area.

5. $w_{ij}=\left\{\begin{array}{c}1/d_{ij}^{2}i\ne j\\ 0i=j\end{array}\right.$

6. LM test: for testing spatial lag term and spatial error term, respectively.

7. Robust-LM test: still for testing spatial lag term and spatial error term, respectively.

8. The list of sample countries (regions) is in Table A1 in the Appendix.

9. To save space, we place the estimation results of spatial regression model’s coefficients in the Appendix.

10. To save space, we only report the estimation results of GVC participation variables (entity, forward, and backward). The estimation results for control variables are available on request. The detailed industry (sub-industry) list is provided in Appendix Table A7.

11. Ahff: Agriculture, hunting, forestry, and fishing; TM: Total Manufactures; TBSS: Total Business Sector Services; Csps: Community, social, and personal services; Eoe: Electrical and optical equipment; Wpppp: Wood, paper, paper products, printing, and publishing.

12. High-tech manufacturing includes Electrical and optical equipment (Eoe); Medium-high-tech manufacturing includes Chemicals and nonmetallic mineral products (Cnmp), Machinery and equipment (Me), and Transport equipment (Te); Medium-low-tech manufacturing includes Basic metals and fabricated metal products (Bmfmp); Low-tech manufacturing includes Food products, beverages, and tobacco (Fpbt), Textiles, textile products, leather, and footwear (Ttplf), Wood, paper, paper products, printing, and publishing (Wpppp), Manufacturing (nec), and recycling (Mr).

Additional information

Funding

This work was supported by the Chinese National Funding of Social Sciences [18VSJ055]; National Social Science Foundation of China [71573076].