The change in energy and carbon emissions efficiency after afforestation in China by applying a modified dynamic SBM model

Teng, Xiangyu; Liu, Fan‐peng; Chiu, Yung‐ho

doi:10.1016/j.energy.2020.119301

Cited by 58 publications

(19 citation statements)

References 28 publications

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“…Li et al (2019) who found that in subtropical Chinese forests, the stand age of trees, as well as environmental conditions and biodiversity, affects the carbon sucking potential of forests. A recent study done on Chinese data from the years 2007-2017 suggested that with a substantial increase in forests, carbon emission efficiency, as well as energy efficiency, are improved; hence, to deal with the challenges of climate change, afforestation is the best strategy Teng et al (2021). Although the majority of studies try to check the influence of forests on climate, Case et al (2021) conducted a study on American forests to check whether or not climate change also affects the carbon storage potential of these forests.…”

Section: Impact Of Forest On Carbon Emissionmentioning

confidence: 99%

Effects of Forestry on Carbon Emissions in China: Evidence From a Dynamic Spatial Durbin Model

Mighri

Chen

2021

Front. Environ. Sci.

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Research has proved the significance of forests in controlling carbon emissions, however, our research sheds light on the management of existing forests to combat climate change. To examine the role of forestation and forest investment activities, dynamic spatial techniques are used for 30 provinces of China. The results suggest that forest investment and management not only reduce carbon locally but also in neighboring provinces. Furthermore, the findings of the current study confirmed that forest investment is the most viable practice to control carbon emissions in China instead of just increasing total forest area. Reforms regarding the management of forests would be a good policy for both pollution reduction and employment generation.

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Section: Impact Of Forest On Carbon Emissionmentioning

confidence: 99%

Effects of Forestry on Carbon Emissions in China: Evidence From a Dynamic Spatial Durbin Model

Mighri

Chen

2021

Front. Environ. Sci.

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“…Cui and Varatharajan (2021) introduced non-radial and non-angular SBM models to measure logistics enterprises' transportation efficiency. Teng et al (2021) and Yang et al (2021) used a dynamic SBM model to evaluate regional wastewater treatment efficiency and carbon emission efficiency after afforestation in China, respectively. Jiang (2021) used the SBM model to measure the efficiency of industrial land.…”

Section: Literature Reviewmentioning

confidence: 99%

Carbon emission efficiency measurement and influencing factor analysis of nine provinces in the Yellow River basin: based on SBM-DDF model and Tobit-CCD model

Zhang

2022

Environ Sci Pollut Res

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The Yellow River basin (YRB) is China's most critical energy consumption and coal production area. The improvement of carbon emission reduction efficiency in this area is the key for the Chinese government to achieve the 2030 carbon peak and 2060 carbon neutral ("30.60"). Given this, this study first calculates the carbon emission efficiency of YRB from 2005 to 2019 based on the slack-based measured directional distance function (SBM-DDF) model and combined with Malmquist-Luenberger (ML) index and decomposes the carbon emission efficiency of each province. Then, a panel Tobit model with random effect is constructed to measure the influencing factors and their influence degree of carbon emission efficiency of YRB. Finally, the main influencing factors are selected, and policy suggestions on how to improve the carbon emission efficiency of each province are put forward with the help of the coupling coordination degree (CCD) model. The results show that first, the carbon emission efficiency of each province is significantly different, but it shows a fluctuating upward trend on the whole. Second, the reasons for the rise or decline of the ML index in different provinces are different. Therefore, the development strategies of different provinces should be formulated from the perspective of accelerating technological progress and improving technical efficiency. Finally, the calculation results of influencing factors and coupling coordination degrees show that provinces with high coupling coordination degrees should focus on developing per capita power consumption and controlling per capita power consumption to consolidate the actual urbanization process and industrial structure adjustment. Provinces with low coupling coordination degrees should focus on maintaining the urbanization process and increasing the development of the tertiary industry. Therefore, to fundamentally reduce carbon emissions in YRB areas, we need to consider implementing differentiated emission reduction schemes based on national strategic objectives and in combination with the development characteristics of various provinces.

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“…However, these definitions only consider the proportion of carbon emissions to GDP (or energy consumption) and ignore the multi-dimensional input factors in the actual production process. Recently, most studies estimated CEE from the total factor perspective [ 20 , 21 ]. Common input–output methods include data envelopment analysis (DEA) and stochastic frontier analysis (SFA).…”

Section: Literature Reviewmentioning

confidence: 99%

The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation

Zheng

Cheng

Liu

et al. 2022

IJERPH

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Urban agglomerations have become the core areas for carbon reduction in China since they account for around 75% of its total emissions. Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and the Pearl River Delta (PRD), which are its most important poles of regional development and technological innovation, are key to achieving China’s carbon peak emissions target. Based on the panel data of these three major urban agglomerations from 2003 to 2017, this study estimated the carbon emission efficiency (CEE) by the super-efficiency slacks-based measure (super-SBM) model and analyzed its spatiotemporal distribution pattern. The Dagum Gini coefficient was used to evaluate the difference in CEE between the three major agglomerations, while panel data models were established to analyze the impact of technological innovation on the three agglomerations. The overall CEE showed an upward trend during the study period, with significant spatial and temporal variations. Additionally, the main source of urban agglomeration difference in CEE evolved from inter-regional net differences to intensity of transvariation. While technological innovations are expected to significantly improve CEE, their effect varies among urban agglomerations. These results provide policymakers with insights on the collaborative planning of urban agglomerations and the low-carbon economy.

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The change in energy and carbon emissions efficiency after afforestation in China by applying a modified dynamic SBM model

Cited by 58 publications

References 28 publications

Effects of Forestry on Carbon Emissions in China: Evidence From a Dynamic Spatial Durbin Model

Effects of Forestry on Carbon Emissions in China: Evidence From a Dynamic Spatial Durbin Model

Carbon emission efficiency measurement and influencing factor analysis of nine provinces in the Yellow River basin: based on SBM-DDF model and Tobit-CCD model

The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation

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