Identifying the driving forces of national and regional CO2 emissions in China: Based on temporal and spatial decomposition analysis models

Yang

et al. 2021

Environ Sci Pollut Res

The paper aims to investigate the influencing factors that drive the temporal and spatial differences of CO2 emissions for the transportation sector in China. For this purpose, this study adopts a Logistic Mean Division Index (LMDI) model to explore the driving forces of the changes for the transport sector’s CO2 emissions from a temporal perspective during 2000–2017 and identifies the key factors of differences in the transport sector’s CO2 emissions of China’s 15 cities in four key years (i.e., 2000, 2005, 2010, and 2017) using a multi-regional spatial decomposition model (M-R). Based on the empirical results, it was found that the main forces for affecting CO2 emissions of the transport sector are not the same as those from temporal and spatial perspectives. Temporal decomposition results show that the income effect is the dominant factor inducing the increase of CO2 emissions in the transport sector, while the transportation intensity effect is the main factor for curbing the CO2 emissions. Spatial decomposition results demonstrate that income effect, energy intensity effect, transportation intensity effect, and transportation structure effect are important factors which result in enlarging the differences in city-level CO2 emissions. In addition, the less-developed cities and lower energy efficiency cities have greater potential to reduce CO2 emissions of the transport sector. Understanding the feature of CO2 emissions and the influencing factors of cities is critical for formulating city-level mitigation strategies of the transport sector in China. Overall, it is expected that the level of economic development is the main factor leading to the differences in CO2 emissions from a spatial-temporal perspective.

Section: Methodology Study Area and Datamentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Driving forces of temporal-spatial differences in CO2 emissions at the city level for China’s transport sector

Yang

et al. 2021

Environ Sci Pollut Res

“…Timilsina and Shrestha performed an LMDI analysis of CO 2 emissions in the overall transport sector in different Asian countries and identified different driving forces of CO 2 emissions in these countries [13]. Li et al studied CO 2 emissions performance at both the national and regional levels; they traced the growth trend and spatial disparity of CO 2 emissions in China based on the LMDI method from 2000-2014 [14]. Using the LMDI method, Zhang et al identified the relationships between transport sector energy consumption and changes of the transport mode, passenger-freight share, energy intensity, and transport activity in China between 1980 and 2006 [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Logarithmic Mean Divisia Index Decomposition of CO2 Emissions from Urban Passenger Transport: An Empirical Study of Global Cities from 1960–2001

Bao³

et al. 2019

Sustainability

The urban transport sector has become one of the major contributors to global CO2 emissions. This paper investigates the driving forces of changes in CO2 emissions from the passenger transport sectors in different cities, which is helpful for formulating effective carbon-reduction policies and strategies. The logarithmic mean Divisia index (LMDI) method is used to decompose the CO2 emissions changes into five driving determinants: Urbanization level, motorization level, mode structure, energy intensity, and energy mix. First, the urban transport CO2 emissions between 1960 and 2001 from 46 global cities are calculated. Then, the multiplicative decomposition results for megacities (London, New York, Paris, and Tokyo) are compared with those of other cities. Moreover, additive decomposition analyses of the 4 megacities are conducted to explore the driving forces of changes in CO2 emissions from the passenger transport sectors in these megacities between 1960 and 2001. Based on the decomposition results, some effective carbon-reduction strategies can be formulated for developing cities experiencing rapid urbanization and motorization. The main suggestions are as follows: (i) Rational land use, such as transit-oriented development, is a feasible way to control the trip distance per capita; (ii) fuel economy policies and standards formulated when there are oil crisis are effective ways to suppress the increase of CO2 emissions, and these changes should not be abandoned when oil prices fall; and (iii) cities with high population densities should focus on the development of public and non-motorized transport.

Sustainable Development

Fresh insights for sustainable development: Collaborative governance of carbon emissions based on social network analysis

Wen

et al. 2023

Global climate change caused by carbon emissions has brought severe challenges to the sustainable development of global economy. In this context, this study constructs a weighted and directed carbon emission network for the European Union (EU). We use social network analysis to measure the indicators of the structural characteristics of the EU carbon emission network, and employ cluster analysis to investigate the role of countries in the carbon emission network. Furthermore, we use linear regression to explore the impacts of structural characteristics of carbon emission network on carbon emissions. The results show that, first the carbon emissions ties among the EU countries show a typical network structure during the sample period. Countries such as Germany, France, and Belgium are at the center of the carbon emission network, while Malta, Cyprus, and Croatia are at the edge. Second, different countries play the roles of "net spillover," "broker," "two-way spillover," and "net receiver" in the EU carbon emission network. Last, the increase of network density, the decrease of network hierarchy and the decrease of network efficiency all significantly reduce the carbon emission intensity of the EU, and narrow the spatial difference of carbon emissions among countries. Improving the weighted centrality indicators of nodes in the carbon emission network is helpful to reduce the carbon emission intensity of the EU countries. Based on the findings, this article puts forward policy suggestions for the collaborative governance of EU carbon emission abatement.