China's transportation sector carbon dioxide emissions efficiency and its influencing factors based on the EBM DEA model with undesirable outputs and spatial Durbin model

Zhao, Pengjun; Zeng, Liangen; Li, Peilin; Lu, Haiyan; Hu, Haoyu; Li, Chengming; Zheng, Mengyuan; Li, Haitao; Zhao, Yu; Yuan, Dongning; Xie, Jinxin; Huang, Qi; Qi, Yuting

doi:10.1016/j.energy.2021.121934

Cited by 183 publications

(80 citation statements)

References 69 publications

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“…We confirm the growth trend of transportation carbon dioxide emissions in China, as B, Bo Wang A, et al [60]. However, we find the "inverted U-shaped" relationship between the technological innovation and the carbon emissions of the transportation industry, not the linear relationship as Zhao, P., et al [61]. In addition, we confirm that the factors affecting carbon dioxide emissions in China's transportation are heterogeneous in spatial distribution, similar to Yang, X., et al [62].…”

Section: Discussionsupporting

confidence: 75%

The Carbon Emission Reduction Effect of Technological Innovation on the Transportation Industry and Its Spatial Heterogeneity: Evidence from China

Shi

Chan

et al. 2021

Atmosphere

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The carbon reduction effect of technological innovation in the transportation industry is conducive to China’s anticipated realization of carbon neutrality. Therefore, we evaluated carbon emission reduction effect of technological innovation in the transportation industry in China. Based on the panel data of 30 sample provinces in China (excluding Hong Kong, Macao, Taiwan and Tibet) from 2012 to 2018, using the Moran’I index and Getis-Ord Gi index, this paper analyzes the evolutionary trend and spatial autocorrelation of carbon emission in the transportation industry, and analyzes the impact of technological innovation on carbon emission levels of the transportation industry and its spatiotemporal differences by using the geographical and temporal weighted regression (GTWR) model by using ArcGIS 10.4 software. The conclusions are as follows: The carbon emission level of China’s transportation industry generally has been rising steadily, showing a spatial distribution pattern of high emissions in the east and low emissions in the west. The cold spots are concentrated in the western region, and the hot spots are situated in the central and eastern regions. Technological innovation has a carbon reduction effect on the transportation industry in the eastern and north-eastern regions, while the effect in other regions is not obvious. However, there is an obvious “inverted U-shaped” relationship between technological innovation and the transportation industry’s carbon emissions. The technological innovation in the transportation industry will have a significant carbon reduction effect after breaking through the technical pain points. This carbon reduction effect has a higher effect on the western region than on the eastern region. In addition, the economic development level, the fiscal expenditure proportion of the transportation industry, the higher education level, and the proportion of fixed asset investment in the transportation industry have played a positive role in reducing carbon in the transportation industry, but the spatial heterogeneity of this carbon reduction effect is relatively strong. Therefore, during the “14th Five-Year Plan” development period in China, it is necessary to continuously promote the low-carbon development of the transportation industry with technological innovation, while highlighting the differentiated carbon reduction governance, and consolidating the role of talents and fiscal support.

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Section: Discussionsupporting

confidence: 75%

The Carbon Emission Reduction Effect of Technological Innovation on the Transportation Industry and Its Spatial Heterogeneity: Evidence from China

Shi

Chan

et al. 2021

Atmosphere

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“…In the literature on carbon emission performance accounting, the generally considered important input factors are capital, labor, and energy [ 38 , 39 , 40 , 44 , 45 ]. Drawing lessons from existing research, the capital investment and investment in fixed assets at the end of each city are selected as the labor input, the urban energy consumption as energy input, GDP as the expected output, and carbon emissions as the expected output.…”

Section: Methodsmentioning

confidence: 99%

“…However, there are still limitations, highlighting the following research gaps: (1) the existing literature has mostly focused on single regions, such as provinces, and few have discussed the spatial agglomeration and spatial transfer of urban CEE in the whole country. In China, prefecture-level cities have always been regional political, economic, and cultural centers, accounting for a large proportion of the population and GDP [ 38 ]; (2) existing studies mainly focused on the calculation of the new-type urbanization index, and lack analysis of regional differences and spatial-temporal variation characteristics of the impact of each dimension of urbanization on carbon emissions; (3) in terms of analyzing the influencing factors of CEE, previous literature has mainly adopted traditional econometric methods [ 39 , 40 ], without considering the spatio-temporal heterogeneity. Chinese cities have significant differences in development, and the same influencing factors may have different impacts in different regions [ 41 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Spatio-Temporal Effects of Multi-Dimensional Urbanization on Carbon Emission Efficiency: Analysis Based on Panel Data of 283 Cities in China

Zhou

Lanzhu

Zhao

et al. 2021

IJERPH

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Under the influence of complex urbanization, improving the carbon emission efficiency (CEE) plays an important role in the construction of low-carbon cities in China. Based on the panel data of 283 prefectural-level cities in China from 2005 to 2017, this study evaluated the CEE by the US-SBM model, and explored the spatial agglomeration evolution characteristics of CEE from static and dynamic perspectives by integrating ESDA and Spatial Markov Chains. Then, the spatial heterogeneity of the impacts of multi-dimensional urbanization on CEE were analyzed by using the Geographically and Temporally Weighted Regression (GTWR). The results show that: (1) with the evolution of time, the CEE has a trend of gradual improvement, but the average is 0.4693; (2) from the perspective of spatial static agglomeration, the “hot spots” of CEE mainly concentrated in Shandong Peninsula, Pearl River Delta, and Chengdu-Chongqing urban agglomeration; The dynamic evolution of CEE gradually forms the phenomenon of “club convergence”; (3) urbanization of different dimensions shows spatial heterogeneity to CEE. The impact of economic urbanization in northern cities on CEE shows an inverted “U” shape, and the negative impact of spatial urbanization on CEE appears in the northwest and resource-based cities around Bohai Sea. Population and social urbanization have a positive promoting effect on CEE after 2010. These findings may help China to improve the level of CEE at the city level and provide a reference for low-carbon decision-making.

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“…Work on how to decarbonize specific industries such as iron and steel manufacturing have been reviewed recently by (Z. Fan and Friedmann 2021), deployment of smart energy systems at a regional or national level has also been proposed (Zhao et al 2022;, while (Nabernegg et al 2019) focused on analyzing national policies and how they relate to the emissions resulting from international supply chains. On a national level, the deployment of measures to lower GHG emissions may result in constraining the growth in economy of some countries especially if the most productive sectors are the most pollutive ones.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The use of Data Envelopment Analysis (DEA) (Charnes, Cooper, and Rhodes 1978) which was developed for defining various forms of efficiency through the selection of appropriate input and output performance indicators, has also been used for examining the environmental performance of economies. Examples include relating environmental sustainability and economic growth (Bresciani et al 2021) (and assessing carbon emissions efficiency for sectors of the economy (Zhao et al 2022). Such approaches are useful for identifying empirically efficient examples (i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards a Low Carbon ASEAN: An environmentally extended MRIO optimization model

Amheka

Nguyen

Yu³

et al. 2022

Preprint

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BackgroundEconomic growth is dependent on economic activity, which often translates to higher levels of carbon emissions. With the emergence of technologies that promote sustainable production, governments are working towards achieving their target economic growth while minimizing environmental emissions to meet their commitments to the international community. The IPCC reports that economic activities associated with electricity and heat production contributed most to GHG emissions and it led to the steady increase in global average temperatures. Currently, more than 90% of the total GHG emissions of the ASEAN region is attributable to Indonesia, Malaysia, the Philippines, Thailand, and Vietnam. These regions are also expected to be greatly affected with climate change. To analyze how ASEAN nations can achieve carbon reduction targets while aspiring for economic growth rates, and in consideration of interdependencies between nations, this study develops a multi-regional input-output model for minimizing collective and individual carbon emissions. A high-level eight-sector economy is used for analyzing different economic strategies.ResultsThis model shows that minimizing collective carbon emissions can still yield economic growth. Countries can focus on developing sectors that have growth potentials and lower carbon intensity-such as the services sector, agriculture, and food manufacturing sector. In addition, the simultaneous implementation of multiple carbon emission reduction strategies provides the largest reduction in regional carbon emissions. ConclusionsThis model provides a more holistic view of how the generation of carbon emissions are influenced by the interdependence of nations. The emissions reduction achieved by each country varied depending on the state of technology and the level of economic development in the different regions. This current model can be used to help nations identify more appropriate and achievable carbon reduction targets and to develop more customized policies to target priority sectors in a country. This model is currently limited by the assumption of fixed technical coefficients in the exchange and interdependence of different regions. Future work can investigate modelling flexible multi-regional trade where regions have the option of substituting goods and products in its import or export structure. Other strategies for reducing carbon emission intensity can also be explored, such as modelling transport mode choices, or establishing sectors for waste management. Hybrid models which integrate the multi-regional input-output linear program model with data envelopment analysis can also be developed.

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China's transportation sector carbon dioxide emissions efficiency and its influencing factors based on the EBM DEA model with undesirable outputs and spatial Durbin model

Cited by 183 publications

References 69 publications

The Carbon Emission Reduction Effect of Technological Innovation on the Transportation Industry and Its Spatial Heterogeneity: Evidence from China

The Carbon Emission Reduction Effect of Technological Innovation on the Transportation Industry and Its Spatial Heterogeneity: Evidence from China

Spatio-Temporal Effects of Multi-Dimensional Urbanization on Carbon Emission Efficiency: Analysis Based on Panel Data of 283 Cities in China

Towards a Low Carbon ASEAN: An environmentally extended MRIO optimization model

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