Driving Factors of Regional Transport Carbon Emission Efficiency: Empirical Evidence from China

2021 ◽  
Author(s):  
Peng Jia ◽  
Qifei Ma ◽  
Sujuan Li ◽  
Haibo Kuang
Keyword(s):  
Empirical Evidence ◽  
Carbon Emission ◽  
Driving Factors ◽  
Regional Transport ◽  
Emission Efficiency

scholarly journals Examining the Driving Factors of the Direct Carbon Emissions of Households in the Ebinur Lake Basin Using the Extended STIRPAT Model

2021 ◽  
Vol 13 (3) ◽  
pp. 1339
Author(s):  
Ziyuan Chai ◽  
Zibibula Simayi ◽  
Zhihan Yang ◽  
Shengtian Yang
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Driving Factors ◽  
Series Data ◽  
Lake Basin ◽  
Stirpat Model ◽  
Consumption Structure ◽  
Ebinur Lake ◽  
Ebinur Lake Basin

In order to achieve the carbon emission reduction targets in Xinjiang, it has become a necessary condition to study the carbon emission of households in small and medium-sized cities in Xinjiang. This paper studies the direct carbon emissions of households (DCEH) in the Ebinur Lake Basin, and based on the extended STIRPAT model, using the 1987–2017 annual time series data of the Ebinur Lake Basin in Xinjiang to analyze the driving factors. The results indicate that DCEH in the Ebinur Lake Basin during the 31 years from 1987 to 2017 has generally increased and the energy structure of DCEH has undergone tremendous changes. The proportion of coal continues to decline, while the proportion of natural gas, gasoline and diesel is growing rapidly. The main positive driving factors affecting its carbon emissions are urbanization, vehicle ownership and GDP per capita, while the secondary driving factor is residents’ year-end savings. Population, carbon intensity and energy consumption structure have negative effects on carbon emissions, of which energy consumption structure is the main factor. In addition, there is an environmental Kuznets curve between DCEH and economic development, but it has not yet reached the inflection point.

scholarly journals Modelling Carbon Emissions Efficiency from UK Higher Education Institutions Using Data Envelopment Analysis

2019 ◽  
pp. 1-18
Author(s):  
Adefarati Oloruntoba ◽  
Japhet Tomiwa Oladipo
Keyword(s):  
Higher Education ◽  
Energy Efficiency ◽  
Data Envelopment Analysis ◽  
Carbon Emission ◽  
Scale Effects ◽  
Higher Education Institutions ◽  
High Energy ◽  
Data Envelopment ◽  
Emission Efficiency ◽  
The Uk

Aims: To correlate the energy and carbon emission efficiency relative to research income, gross internal area, and population for all the Higher Education Institutions (HEIs) in the UK and to assess the comparative carbon emission efficiency of HEIs relative to economic metrics. Study Design:  Analytical panel data study. Place and Duration of Study: This paper evaluates the energy efficiency of 131 HEIs in the UK subdivided into Russell and non-Russell groups from 2008 to 2015. Methodology: Data Envelopment Analysis (DEA) and Malmquist productivity indexes (MPI) are used for the efficiency calculations. Results: The empirical results indicate that UK HEIs have relatively high energy efficiency scores of 96.9% and 77.6% (CRS) and 98.5%, 86.3% (VRS) for Russell and non-Russell groups respectively. Conclusion: The evidence from this study reveals that HEIs are not significantly suffering from scale effects, hence, an increase in energy efficiency of these institutions is feasible with the present operating scale but would need to work on their technical improvements in energy use. Malmquist index analysis confirms the lack of substantial technological innovation, which impedes their energy efficiency and productivity gain. Findings show that pure technical efficiency accounts for the annual efficiency obtained in the DEA model, the technological progress in contrast is the source of their energy inefficiency.

scholarly journals Does Emission Trading Improve Carbon Emission Efficiency of China’s Iron and Steel Industry? An Exploration Using a DEA-SBM and Difference-In-Differences Approach

2021 ◽  
Author(s):  
Xiping Wang ◽  
Sujing Wang
Keyword(s):  
Steel Industry ◽  
Carbon Emission ◽  
Emission Trading ◽  
Energy Structure ◽  
Iron And Steel Industry ◽  
Low Carbon ◽  
Difference In Differences ◽  
Iron And Steel ◽  
Emission Efficiency ◽  
The Impact

Abstract As an effective tool of carbon emission reduction, emission trading has been widely used in many countries. Since 2013, China implemented carbon emission trading in seven provinces and cities, with iron and steel industry included in the first batch of pilot industries. This study attempts to explore the policy effect of emission trading on iron and steel industry in order to provide data and theoretical support for the low-carbon development of iron and steel industry as well as the optimization of carbon market. With panel data of China’s 29 provinces from 2006 to 2017, this study adopted a DEA-SBM model to measure carbon emission efficiency of China’s iron and steel industry (CEI) and a difference-in-differences (DID) method to explore the impact of emission trading on CEI. Moreover, regional heterogeneity and influencing mechanisms were further investigated, respectively. The results indicate that: (1) China's emission trading has a significant and sustained effect on carbon abatement of iron and steel industry, increasing the annual average CEI by 12.6% in pilot provinces. (2) The policy effects are heterogeneous across diverse regions. Higher impacts are found in the western and eastern regions, whereas the central region is not significant. (3) Emission trading improves CEI by stimulating technology innovation, reducing energy intensity, and adjusting energy structure. (4) Economic level and industrial structure are negatively related to CEI, while environmental governance and openness degree have no obvious impacts. Finally, according to the results and conclusions, some specific suggestions are proposed.

Sign in / Sign up

Export Citation Format

Share Document