The Impacts of Freight Trade on Carbon Emission Efficiency: Evidence from the Countries along the “Belt and Road”

Abstract

Extensive research has been carried out on the “Belt and Road” initiative, most of it focusing on geographical economy and international trade. However, there is a lack of research on the carbon emissions efficiency of the countries along the “Belt and Road,” especially regarding the impact of freight trade. To address this research gap, this paper first employs a metafrontier nonradial directional distance function to measure the carbon emission efficiency of 32 countries along the “Belt and Road” from 1990 to 2014. It then examines the role of freight trade. Our main research findings are as follows. Firstly, the carbon emission efficiency of the countries along the “Belt and Road” is generally low. Among them, Russia and Central Asia are mainly due to the large between-group gap in carbon emission efficiency, while Southeast Asia, Western Asia and North Africa, East Asia, South Asia, and Central and Eastern Europe are mainly due to the large within-group gap. Secondly, freight trade promotes carbon emission efficiency, but it will aggravate the gap between the contemporaneous technology and the group technology. Freight trade mainly promotes the contemporaneous carbon emission efficiency (CTCEI) and group-frontier carbon emission efficiency (ITCEI) of low fossil energy dependent countries, and the metafrontier carbon emission efficiency (GTCEI) of high fossil energy dependent countries. Thirdly, foreign direct investment (FDI) has a significant negative effect on a host country’s ITCEI and GTCEI, and it will decrease the gap between the group technology and the metafrontier technology. However, freight trade can effectively prevent the entry of FDI, thereby indirectly improving carbon emission efficiency and reducing carbon emission gap.

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“…The regression coefficient of technological innovation is positive at a significant level of 1% in the overall sample regression of the Eastern region; however, it is insignificant in the regression of the Central and Western regions. Hu et al 40 also draw a similar conclusion. Furthermore, the level of industrialization and population density exerts an inhibitory effect on carbon emission efficiency but is statistically insignificant.…”

“…Existing research on carbon emission efficiency primarily focuses on influencing factors, regional differences, and measurement methods. [40][41][42] Wang et al 43 utilized the dynamic panel threshold regression model to explore the impact of official development assistance (ODA) on carbon emissions in 59 countries. Meng and Niu 44 discovered that the effect of technological innovation on carbon emission efficiency is significantly greater than the upgrading of industrial structure.…”

“…Input and output variables: Based on the existing research, when calculating carbon emission efficiency, the input factors majorly include capital, labor, and energy, whereas the output includes expected and undesired outputs. 40,66 Among them, capital input is represented by the capital stock. Because official survey data of the capital stock in China are currently unavailable, this work utilizes the perpetual inventory method to estimate the capital stock of cities.…”

“…Based on existing studies, the urban economic development level (GDP), industrial structure (ST), industrialization level (IN), technological innovation (TE), and population density (DP) are the control variables. 40,72 Among them, the level of urban economic development is measured by the natural logarithm of the urban per capita GDP. The industrial structure is measured by the industrial structure upgrading index, i.e., S = r1 * 1 + r2 * 2 + r3 * 3, where S represents the industrial structure upgrading index; r1, r2, and r3 are the proportion of the output value of the primary industry, the secondary industry and the tertiary industry in the urban GDP, respectively.…”

“…Sun et al 49 believe that there is a positive correlation between the global value chain (GVC) position index and carbon emission efficiency, i.e., if a country has a high position in the GVC, its carbon emission efficiency is high as well. Hu et al 40 used the non-parametric DEA method to measure carbon emission efficiency. Consequently, the calculation outcomes showed that although China's carbon emission efficiency is annually improving, it is still at a relatively low level.…”