Carbon emission curbing effects and influencing mechanisms of China’s Emission Trading Scheme: The mediating roles of technique effect, composition effect and allocation effect

Chen, Shuo; Siekelová, Anna; Wang, Xin

doi:10.1016/j.jclepro.2020.121700

Cited by 120 publications

(51 citation statements)

References 63 publications

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“…On the other hand, the main conclusion is different from the previous studies of Lyu et al, 10 Chen et al, 22 Wang et al 24 The reason of this finding can be explained by the differences of research sample, time interval and measurement of green innovation. Specifically, compared with the studies of Lyu et al, 10 Chen et al, 22 the panel dataset used in this paper consist of 277 cities with a time span from 2003 to 2017, which can identify the effect of ETS on regional green innovation from a more micro perspective and longer period. Moreover, this paper is different from Lyu et al, 10 Chen et al, 22 Wang et al, 24 in the measurement of green innovation.…”

Section: Conclusion and Policy Implicationscontrasting

confidence: 74%

Does a carbon emissions trading scheme spur urban green innovation? Evidence from a quasi-natural experiment in China

Li²,

Song

et al. 2021

Energy & Environment

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Based on the panel data of 277 cities between 2003 and 2017 and a unique city-level dataset of green patent applications, this study employs the difference-in-differences (DID) method to evaluate the effect of China’s carbon emission trading scheme (ETS) pilots on urban green innovation. The findings indicate that China’s ETS pilots have a positive impact on urban green innovation, and that impact is more significant for municipalities than for prefecture-level cities. Furthermore, the impact on different categories of urban green innovation is heterogeneous. More specifically, China’s ETS pilots have significantly spurred urban green innovation that is closely related to energy conservation and emission reduction, including alternative energy production, transportation, energy conservation and so forth. Moreover, the facilitating effect of China’s ETS pilots on urban green innovation suffers from a lagging effect, which began to show a significant positive effect in 2016. Overall, this paper identifies the effect of China’s ETS pilots on urban green innovation, and suggests that the government should consider the heterogeneity of urban green innovation when designing national ETS policies.

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Section: Conclusion and Policy Implicationscontrasting

confidence: 74%

Does a carbon emissions trading scheme spur urban green innovation? Evidence from a quasi-natural experiment in China

Li²,

Song

et al. 2021

Energy & Environment

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“…is means that tightening environmental regulation is an effective measure of guaranteeing economic growth and optimizing environmental quality [18]. Using data of 36 industrial sectors from 2000 to 2014, Chen et al [19], found that, in general, despite a significant carbon reduction effect by current policy, there is still much space for potential abatement to be developed. Li and Wu [20] show that the effect of local environmental regulation on green total factor productivity of China is significantly positive in high political attribute cities but negative in lower political attribute cities.…”

Section: Low-carbon Policy and Gtfpmentioning

confidence: 99%

Industrial Structure, R&D Staff, and Green Total Factor Productivity of China: Evidence from the Low‐Carbon Pilot Cities

et al. 2021

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Using data of 26 cities in China from 2004 to 2017, the green total factor productivity is investigated by the SMM-GML method. The corresponding empirical analysis is conducted with the DID model. This paper investigates the relation between low-carbon pilot policy (LCC) and green total factor productivity and discusses the mediating effect of industrial structure and the number of R&D staff (RDS). First, we find that LCC has a significant effect on pilot cities’ GTFP. And, it also promotes GTFP via industrial structure. Second, LCC can improve industrial structure optimization and realization, and industrial structure realization affects GTFP significantly, while optimization cannot. Third, LCC cannot attract more RDS, and RDS harms local GDFP because of talent misallocation. At last, the rate of GTFP presented different upward trends in the order of non-eastern cities and eastern cities. The effect of LCC on GTFP is significant in non-eastern cities, but not eastern ones, which clearly demonstrates the imbalanced development of the green economy. Therefore, the governments of eastern and non-eastern regions should adopt different measures based on local conditions in industrial structure transformation and recruitment and strengthen environmental regulations to make the effect of the low-carbon policy lasting and promote GTFP growth balance in all regions.

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“…This can be reduced by improving the energy use efficiency, which is an important reference factor for effective emission reduction (Wei et al, 2021). Technology is directly linked to the carbon emission intensity, which suppresses carbon emissions by improving the production capacity and equipment efficiency to achieve an effective use of energy (Chen et al, 2020). The contribution of the economic effect to emission reduction is as high as 57.62%, which is the main driving factor of the increase in carbon emissions (Dong et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Carbon Emissions in the Xinjiang Production and Construction Corps and Driving Factors

et al. 2021

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Xinjiang production and Construction Corps (XPCC) is an important provincial administration in China and vigorously promotes the construction of industrialization. However, there has been little research on its emissions. This study first established the 1998-2018 XPCC subsectoral carbon emission inventory based on the Intergovernmental Panel on Climate Change (IPCC) carbon emission inventory method and adopted the logarithmic mean Divisia indexmethod (LMDI) model to analyze the driving factors. The results revealed that from 1998 to 2018, the total carbon emissions in the XPCC increased from 6.11 Mt CO2 in 1998 to 115.71 Mt CO2 in 2018. For the energy structure, raw coal, coke and industrial processes were the main contributors to carbon emissions. For industrial structure, the main emission sectors were the production and supply of electric power, steam and hot water, petroleum processing and coking, raw chemical materials and chemical products, and smelting and pressing of nonferrous metals. In addition, the economic effect was the leading factor promoting the growth of the corps carbon emissions, followed by technical and population effects. The energy structure effect was the only factor yielding a low emission reduction degree. This research provides policy recommendations for the XPCC to formulate effective carbon emission reduction measures, which is conducive to the construction of a low-carbon society. Moreover, it is of guiding significance for the development of carbon emission reduction actions for the enterprises under the corps and provides a reference value for other provincial regions.

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Carbon emission curbing effects and influencing mechanisms of China’s Emission Trading Scheme: The mediating roles of technique effect, composition effect and allocation effect

Cited by 120 publications

References 63 publications

Does a carbon emissions trading scheme spur urban green innovation? Evidence from a quasi-natural experiment in China

Does a carbon emissions trading scheme spur urban green innovation? Evidence from a quasi-natural experiment in China

Industrial Structure, R&D Staff, and Green Total Factor Productivity of China: Evidence from the Low‐Carbon Pilot Cities

Carbon Emissions in the Xinjiang Production and Construction Corps and Driving Factors

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