The path of technological progress for China's low-carbon development: Evidence from three urban agglomerations

Kang, Zhuang; Li, Ke; Qu, Jianying

doi:10.1016/j.jclepro.2018.01.027

Cited by 60 publications

(27 citation statements)

References 43 publications

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“…This result shows that under the low-carbon development requirements, the economy has gradually decoupled from energy consumption. The path of technical progress is highly related to the economy, agreed to Kang et al [41], and its contribution rate has increased from 0.30% to 0.44%. It shows that the more economically developed, more funds can be introduced to develop new technologies, use energy-saving machines, which can reduce carbon emissions fundamentally.…”

Section: Comparison Of Base Development Scenario and Low Carbon Scenariomentioning

confidence: 85%

Analysis of the Influence Mechanism of Energy-Related Carbon Emissions with a Novel Hybrid Support Vector Machine Algorithm in Hebei, China

Wang¹,

Yang²,

Zhang³

2019

Pol. J. Environ. Stud.

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China has pledged to the world that the intensity of energy consumption and the intensity of CO 2 emissions are used as binding indicators, and that the carbon intensity of GDP in 2020 will be reduced by 40-45% compared with 2005. In order to achieve the early arrival of carbon emission peaks, the Chinese government has formulated another constraint indicator. The 13th Five-Year Plan for controlling greenhouse gas (GHG) emissions requires that the carbon intensity of the Beijing-Tianjin-Hebei region (BTH) in 2020 fall by 20.5% from 2016. BTH is China's political and cultural center and an important core area of northern China's

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Section: Comparison Of Base Development Scenario and Low Carbon Scenariomentioning

confidence: 85%

Analysis of the Influence Mechanism of Energy-Related Carbon Emissions with a Novel Hybrid Support Vector Machine Algorithm in Hebei, China

Wang¹,

Yang²,

Zhang³

2019

Pol. J. Environ. Stud.

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show abstract

“…As for technological innovation, which is also called technological progress, scholars have conducted many studies about its impact on carbon emissions from different aspects. For example, Kang et al [17] explored the various impacts of different sources of technological progress (including R&D, FDI and international trade) on the low-carbon development based on the panel data of 50 cities across the year of 2005-2014 in China's three urban agglomerations. Wang et al [18] employed patent data to measure technological progress and identified its heterogeneous influence on carbon emissions in different economic sectors of China using the panel data of 289 Chinese cities.…”

Section: Literature Review a The Effect Of Technological Innovatmentioning

confidence: 99%

Is Technological Innovation Effective for Energy Saving and Carbon Emissions Reduction? Evidence From China

Zhang

Yin

2020

IEEE Access

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Technological innovation is increasingly becoming an important way to develop a lowcarbon economy. Recently, exploring the driving path of low-carbon development has become the hot topic, however, the role of energy consumption in the carbon-reduction effect of technological innovation has been ignored. Based on China's provincial-level panel data from 2005 to 2016, this paper investigates the effect of technological innovation on energy saving and carbon emissions reduction and the role of energy consumption in the influencing process by using linear regression model, mediating effect model and threshold regression model. The results show that technological innovation has effectively promoted energy saving and carbon emissions reduction in China, with different effect coefficients between the three areas of China. Moreover, energy consumption plays a significant partial mediating role in the effect of technological innovation on carbon emissions reduction. Finally, the effect of technological innovation on carbon emissions is limited by energy consumption. Low energy consumption significantly promotes a reduction in carbon emissions through technological innovation. Once energy consumption exceeds a critical level, the promoting effect of technological innovation on carbon emissions reduction will turn into the inhibiting effect. Based on these findings, some policy recommendations are put forward to accelerate the low-carbon transformation of the economy. INDEX TERMS Technological innovation, energy saving, carbon emissions reduction, threshold regression model, China.

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“…Each system-forming area has a key indicator, which is characterized by both the current state (static) and the indicator of its change (dynamic). For energy, the main sign from the green growth point of view is to determine energy intensity Golubetskaya [30], Porfiriev [31], Andreas, Burns, Touza [32], Kang, Li, Qu [33], and in this interpretation it should be considered with renewable and non-renewable sources. Similarly, in "transport" subsystem, detailing the indicators and focusing on the electric and other more ecological drive are not necessary, as the most important indicator is not actually the transition to a new drive, but the emissions and waste from technical operation.…”

Section: Second Level Subsystemmentioning

confidence: 99%

A Study on Green Economy Indicators and Modeling: Russian Context

et al. 2019

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This article aims to assess and forecast the dynamics of a regional green economy. The research relevance is determined by the need to develop theoretical and methodological basis of the green economy for the transition period and to identify criteria basis for assessing the state and regional level of it. The authors applied the modern methods, which allowed to model criteria considering data uncertainty and both static and dynamic criteria. The research process involved the methods of scientific analysis, comparison and synthesis, the theory of fuzzy sets, and fuzzy modeling. The main principles and methodology of the criteria evaluation for a regional green economy are proposed. The principal methodological approach in this research combines the current state and dynamics of the green economy in evaluating and forecasting the conditions of data uncertainty. The research results form a theoretical, methodological, and practical basis for assessing the current state and level of a regional green economy development, determining the effectiveness of environmental and economic programs, optimizing financial management, conducting environmental monitoring, and developing state plans.

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The path of technological progress for China's low-carbon development: Evidence from three urban agglomerations

Cited by 60 publications

References 43 publications

Analysis of the Influence Mechanism of Energy-Related Carbon Emissions with a Novel Hybrid Support Vector Machine Algorithm in Hebei, China

Analysis of the Influence Mechanism of Energy-Related Carbon Emissions with a Novel Hybrid Support Vector Machine Algorithm in Hebei, China

Is Technological Innovation Effective for Energy Saving and Carbon Emissions Reduction? Evidence From China

A Study on Green Economy Indicators and Modeling: Russian Context

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