Effect of technological progress on carbon emissions: New evidence from a decomposition and spatiotemporal perspective in China

Huang, Junbing; Xiang, Chen; Yu, Keming; Cai, Xiaochen

doi:10.1016/j.jenvman.2020.110953

Cited by 78 publications

(24 citation statements)

References 33 publications

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“…For example, Chen et al (2021) analyzed the temporal and spatial characteristics of industrial carbon emissions in four regions of Guangdong province from 2005 to 2015, and concluded that industrial carbon emissions have a trend of eastward expansion [7]. The spatial dynamic analysis model (SDDM) was used to study the impact of different technological progress factors on carbon emissions [8]. Cui et al (2021) explored spatiotemporal dynamic evolution of carbon emission intensity and per capita carbon emissions from planting industry in 31 provinces in China across 20 years.…”

Section: Literature Review 21 Spatiotemporal Analysis Of Carbon Emissionmentioning

confidence: 99%

Spatiotemporal Analysis of Influencing Factors of Carbon Emission in Public Buildings in China

Liu

Zhang

2022

Buildings

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The rapid development of public buildings has greatly increased the country’s energy consumption and carbon emissions. Excessive carbon emissions contribute to global warming. This paper aims to measure the carbon emissions in the operation of public buildings, and to identify the multiple influencing factors of carbon emissions in operational public buildings. First, the spatial and temporal variation characteristics of carbon emissions from public buildings in 30 provinces of China from 2008–2019 are analyzed. Second, a green building index is constructed, and the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model is utilized to explore the relationship between each influencing factor and carbon emissions, using spatial and temporal geographically weighted regression analysis. The results show that the effects of population, urbanization rate, GDP per capita, green building index, and industrial structure on carbon emissions from public buildings all show spatial correlation and differences. There are east-west differences in the operational carbon emissions of public buildings in China’s provinces. Cluster evolution shows a spatially increasing trend from west to east. To some extent, policymakers can develop appropriate policies for different provinces through the findings.

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Section: Literature Review 21 Spatiotemporal Analysis Of Carbon Emissionmentioning

confidence: 99%

Spatiotemporal Analysis of Influencing Factors of Carbon Emission in Public Buildings in China

Liu

Zhang

2022

Buildings

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“…Technological progress, especially green technological progress, can effectively reduce the intensity of pollution emissions (Acemoglu et al 2012 ; Chen et al 2019 ), and it is also an important means to alleviate air pollution and improve environmental quality (Huang et al 2020 ; Bai et al 2020 ). However, green technology innovation generally has a “Curse to the Late Comer.” If an enterprise invests many resources in the R&D of green technologies in the early stage, it may squeeze out conventional technology input, resulting in reducing economic output or even inhibiting economic growth in an environment where conventional technology dominates.…”

Section: The Technology Progress Effect Of Economic Growth Expectatiomentioning

confidence: 99%

Section: The Technology Progress Effect Of Economic Growth Expectations On Air Pollutionmentioning

confidence: 99%

The air pollution effect of government economic growth expectations: evidence from China’s cities based on green technology

Wang

Dong

et al. 2021

Environ Sci Pollut Res

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Based on the Government Work Report of 265 prefecture-level cities in China from 2004 to 2015, this article combines economic growth expectations data and keyword capture to explore the relationship between government economic growth expectations and air pollution. The main results are as follows: (1) The economic growth expectations of local governments, and the "increment" between prefecture-level and provincial governments' growth expectations for economic growth have significantly increased air pollution. The certainty and completion degree of economic growth expectations have different effects on air pollution, and the impact of the expected rigid constraint and overfulfilled degree on air pollution are prominent. When the city's real economic growth exceeds 1%, 2%, 3%, and 4% of growth expectations, respectively, SO 2 emissions will increase by 10.577%, 10.671%, 11.825%, and 16.296%, and PM 2.5 concentration will increase by 2.115%, 2.503%, 3.592%, and 4.421%.(2) The government's annual economic growth expectations have different effects on different types of technological innovation. For every 1% increase in the government's economic growth expectations, the green technology will be reduced by 0.956% which exacerbates regional air pollution. Furthermore, the green technology can explain 6.5% of air pollution induced by government economic growth expectations.

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“…Although the economy [16], technology [17], urbanization, industrial structure, trade openness [18][19][20], energy consumption [21][22][23], and financial development [11] have been considered in various previous studies, the analysis of both urbanization and industrial structure has not been addressed. Moreover, the optimization of urbanization and industrial structure can help achieve national targets for reducing carbon intensity.…”

Section: Introductionmentioning

confidence: 99%

Impact of Urbanization and Industrial Structure on Carbon Emissions: Evidence from Huaihe River Eco-Economic Zone

Pang

Zhou

Zhao

et al. 2021

Land

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Rapid urbanization in China greatly contributes to carbon emissions, while the industrial structure greatly contributes to changes in the variation of carbon emissions. This research addresses the impact of urbanization and industrial structure on carbon emissions from 2010 to 2018, by focusing on the Huaihe River Eco-economic Zone, which is an important economic corridor along the north–south division of China. Although many studies have focused on investigating the impact of urbanization or industrial structure on carbon emissions, few studies further addressed an analysis of the impact of both on carbon emissions, using multiple measurement models. This paper reveals the holistic and local impact of industrial structure and urbanization on carbon emissions, by integrating a threshold regression model with geographically weighted regression. The results are as follows: (1) From a holistic point of view, industrial structure and urbanization had both, single threshold, and double threshold effects on carbon emissions in the Huaihe River Eco-economic Zone. (2) From a regional perspective, the coefficients of industrial structure on carbon emissions were all positive, but the rate of increase gradually slowed down. The coefficients of urbanization on carbon emissions were all negative, reaching a maximum value of negative effect in 2013. Understanding the holistic and local impact of urbanization and industrial structure on carbon emissions provides governments with differentiated and forward-looking suggestions for mitigating carbon emissions in the Huaihe River Eco-economic Zone.

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Effect of technological progress on carbon emissions: New evidence from a decomposition and spatiotemporal perspective in China

Cited by 78 publications

References 33 publications

Spatiotemporal Analysis of Influencing Factors of Carbon Emission in Public Buildings in China

Spatiotemporal Analysis of Influencing Factors of Carbon Emission in Public Buildings in China

The air pollution effect of government economic growth expectations: evidence from China’s cities based on green technology

Impact of Urbanization and Industrial Structure on Carbon Emissions: Evidence from Huaihe River Eco-Economic Zone

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