How does foreign trade influence China’s carbon productivity? Based on panel spatial lag model analysis

Zhang, Lulu; Xiong, Lichun; Cheng, Baodong; Yu, Chang

doi:10.1016/j.strueco.2018.08.008

Cited by 96 publications

(34 citation statements)

References 28 publications

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“…Using the Malmquist index, Yu (2017) studied the carbon productivity of the transportation industry. The second method is the econometric analysis method (Cole et al, 2013;Li & Wang, 2019;Yan et al, 2020;Yin et al, 2015;Zhang et al, 2018a;Du et al,2019). Du et al (2019) use panel data that included 71 economies to test if the effect of green technology innovation on carbon productivity is significant for economies with high income and not significant for less developed economies.…”

Section: Literature Reviewmentioning

confidence: 99%

Role of Low-carbon Technology Innovation in Environmental Performance of Manufacturing: Evidence From Oecd Countries

Shi

Chen

Zhao

2021

Preprint

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Climate change disrupts the balance of natural ecosystems and threatens the sustainable development of human society. As the leading industry in many countries, manufacturing promotes economic development; unfortunately, it also emits large quantities of greenhouse gases. Thus, it is necessary to transform the production pattern of manufacturing into green production. Although technology innovation is the only way to tackle the issue, different types of technology innovation may lead to different environmental performances. We argue that low-carbon technology innovation (LCTI) is the key to green production. Using data of Economic Co-operation and Development (OECD) countries from 1990 to 2014, we use the patent-stock method to measure LCTI levels and analyze its development trend in OECD countries. Based on the shepherd distance function, we measure carbon efficiency and carbon productivity by the fixed-effect Stochastic Frontier Analysis (SFA) model. Then we investigate the effect of LCTI on carbon emission efficiency in manufacturing by the fixed-effect regression model. After controlling some variables, evidence shows a significant positive influence of LCTI on the environmental performance of manufacturing. The level of LCTI constantly increased in OECD countries during the study period. Among these countries, the level of low-carbon technology in the chemical industry is the highest; and in most of the countries, the low-carbon technology of production process grows fastest. Policy implications are further discussed.

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Section: Literature Reviewmentioning

confidence: 99%

Role of Low-carbon Technology Innovation in Environmental Performance of Manufacturing: Evidence From Oecd Countries

Shi

Chen

Zhao

2021

Preprint

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“…It is estimated that China needs to increase its carbon productivity by a factor of 10 times to achieve the greenhouse gas emission target of IPCC2025 [ 45 ]. The existing literature has found carbon productivity is affected by economic scale [ 46 ], green capital investment [ 47 ], technological innovation level [ 48 ], trade openness degree [ 12 , 49 ], energy consumption structure [ 47 ], and urbanization level [ 13 ]. However, few have analyzed the effect of ETS on carbon productivity and the associated mediators.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the meanwhile, China faces a major challenge of achieving energy conservation and emission reduction without sacrificing economic development as a developing economy with a population of 1.4 billion [ 9 , 10 , 11 ]. To tackle this challenge, increasing carbon productivity has been identified as an effective way [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Does Emission Trading Boost Carbon Productivity? Evidence from China’s Pilot Emission Trading Scheme

Zhou

Liang

Zhou

et al. 2020

IJERPH

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As the country with the largest carbon emissions globally, the effective operation of China’s carbon emissions trading scheme (ETS) is of great importance to the global community in terms of mitigating climate change. This paper considers China’s pilot ETS launched in 2013 as a quasi-natural experiment. Exploring provincial industrial-level data that are more in line with the ETS coverage, the difference-in-difference-in-difference (DDD) model is used to evaluate the impact of the ETS on carbon productivity. Considering different pilot regions and industries, we also analyze the heterogeneous effect of ETS. Moreover, the mediating effects of technical progress and capital investment are explored. We find that China’s pilot ETS boosted carbon productivity. Among pilot regions, the best policy effectiveness appeared in Beijing, while the weakest effectiveness appeared in Chongqing. Among the pilot industries, the pilot ETS had better effectiveness in petrochemical and electric power industries and weaker effectiveness in building materials and transportation industries. Additionally, the pilot ETS promoted carbon productivity through both technological progress and capital investment, and the former contributed more. Our findings can provide empirical references and policy implications for nationwide implementation of ETS to further promote low-carbon economic transformation.

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“…The degree of intermediary centrality reflects the extent to which a city in the network can control the relationship between the other cities. The greater the degree of intermediary centrality, the stronger the "intermediary" role the city plays in the network, thus the greater the extent to which it can control the correlation of carbon emissions of other cities [42].…”

Section: Characteristic Index Of the Spatial Network Of Carbon Emissionsmentioning

confidence: 99%

Research on the Spatial Network Characteristics and Synergetic Abatement Effect of the Carbon Emissions in Beijing–Tianjin–Hebei Urban Agglomeration

Feng

et al. 2019

Sustainability

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Based on the carbon emission data in the Beijing–Tianjin–Hebei urban agglomeration from 2007 to 2016, this paper used the method of social network analysis (SNA) to investigate the spatial correlation network structure of the carbon emission. Then, by constructing the synergetic abatement effect model, we calculated the synergetic abatement effect in the cities and we empirically examined the influence of the spatial network characteristics on the synergetic abatement effect. The results show that the network density first increased from 0.205 in 2007 to 0.263 in 2014 and then decreased to 0.205 in 2016; the network hierarchy fluctuated around 0.710, and the minimum value of the network efficiency was 0.561, which indicates that the network hierarchy structure is stern and the network has good stability. Beijing and Tianjin are in the center of the carbon emission spatial network and play important “intermediary” and “bridge” roles that can have better control over other carbon emission spatial spillover relations between the cities, thus the spatial network of carbon emissions presents a typical “center–periphery” structure. The synergetic abatement effect increased from −2.449 in 2007 to 0.800 in 2011 and then decreased to −1.653 in 2016; the average synergetic effect was −0.550. This means that the overall synergetic level has a lot of room to grow. The carbon emission spatial network has a significant influence on the synergetic abatement effect, while increasing the network density and the network hierarchy. Decreasing the network efficiency will significantly enhance the synergetic abatement effect.

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How does foreign trade influence China’s carbon productivity? Based on panel spatial lag model analysis

Cited by 96 publications

References 28 publications

Role of Low-carbon Technology Innovation in Environmental Performance of Manufacturing: Evidence From Oecd Countries

Role of Low-carbon Technology Innovation in Environmental Performance of Manufacturing: Evidence From Oecd Countries

Does Emission Trading Boost Carbon Productivity? Evidence from China’s Pilot Emission Trading Scheme

Research on the Spatial Network Characteristics and Synergetic Abatement Effect of the Carbon Emissions in Beijing–Tianjin–Hebei Urban Agglomeration

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