Unveiling the driving factors of carbon emissions from industrial resource allocation in China: A spatial econometric perspective

Wang, Bin; Yu, Minxiu; Zhu, Yunhua; Pin-juan, Bao

doi:10.1016/j.enpol.2021.112557

Cited by 83 publications

(27 citation statements)

References 37 publications

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“…Since China’s existing statistics do not include data on carbon emissions in various provinces, we estimate carbon emissions as the product of the consumption of various energy sources and the corresponding carbon emission coefficients, which is a common method to measure carbon emissions [ 9 , 11 , 82 , 83 , 84 , 85 , 86 , 87 ]. Based on the energy consumption data of the China Energy Statistical Yearbook and the practices of Wei and Li [ 88 ] and Liang et al [ 89 ], we select 14 types of fossil fuels to calculate the CDE:

where CE means CDE,

, and

represent the consumption, the average net calorific value, and the carbon dioxide emission factor of energy fuel

, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Over the past few decades, China’s rapid economic development has brought about a series of side effects such as resource depletion and environmental degradation, which makes the sustainable development of China’s economy and society a huge challenge [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Carbon dioxide emissions (CDE) are a key factor in climate deterioration [ 8 , 9 ]. China, as the largest CDE producer in the world and one of the countries committed to achieving carbon neutrality and complete elimination of carbon emissions by 2060 [ 10 ], is under tremendous pressure to reduce emissions [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

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The Impact of Green Technology Innovation on Carbon Emissions in the Context of Carbon Neutrality in China: Evidence from Spatial Spillover and Nonlinear Effect Analysis

Zeng

et al. 2022

IJERPH

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The Paris agreement is a unified arrangement for the global response to climate change and entered into force on 4 November 2016. Its long-term goal is to hold the global average temperature rise well below 2 °C. China is committed to achieving carbon neutrality by 2060 through various measures, one of which is green technology innovation (GTI). This paper aims to analyze the levels of GTI in 30 provinces in mainland China between 2001 and 2019. It uses the spatial econometric models and panel threshold models along with the slack based measure (SBM) and Global Malmquist-Luenberger (GML) index to analyze the spatial spillover and nonlinear effects of GTI on regional carbon emissions. The results show that GTI achieves growth every year, but the innovation efficiency was low. China’s total carbon dioxide emissions were increasing at a marginal rate, but the carbon emission intensity was declining year by year. Carbon emissions were spatially correlated and show significant positive agglomeration characteristics. The spatial spillover of GTI plays an important role in reducing carbon dioxide emissions. In the underdeveloped regions in China, this emission reduction effect was even more significant.

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where CE means CDE,

, and

represent the consumption, the average net calorific value, and the carbon dioxide emission factor of energy fuel

, respectively.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Impact of Green Technology Innovation on Carbon Emissions in the Context of Carbon Neutrality in China: Evidence from Spatial Spillover and Nonlinear Effect Analysis

Zeng

et al. 2022

IJERPH

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show abstract

“…The spatial density of SO 2 emissions shifted from south to north in China, and the scope of agglomeration changed from 2007 to 2018. (2) The results of the standard deviation ellipse of 270 cities in China implied that the spatial distribution direction of SO 2 emissions was "northeast-southwest". The center of the SO 2 emissions standard deviation ellipse shifted to the northeast, from Zhumadian City to Zhoukou City in Henan Province.…”

Section: Conclusion and Suggestionsmentioning

confidence: 99%

“…The main type of environmental pollution in China is industrial pollution, and these pollutants are divided into three states: stationary, liquid, and gaseous. Among them, the impact of gaseous pollution on the public is particularly obvious [1][2][3]. In the winter of 2013, severe haze events impacted many parts of China on a large scale.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO2 Emissions in Chinese Cities: Fresh Evidence from MGWR

Yuan

Sun

Yu³

et al. 2021

Sustainability

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In this study, based on the multi-source nature and humanities data of 270 Chinese cities from 2007 to2018, the spatio-temporal evolution characteristics of SO2 emissions are revealed by using Moran’s I, a hot spot analysis, kernel density, and standard deviation ellipse models. The spatial scale heterogeneity of influencing factors is explored by using the multiscale geographically weighted regression model to make the regression results more accurate and reliable. The results show that (1) SO2 emissions showed spatial clustering characteristics during the study period, decreased by 85.12% through pollution governance, and exhibited spatial heterogeneity of differentiation. (2) The spatial distribution direction of SO2 emissions’ standard deviation ellipse in cities was “northeast–southwest”. The gravity center of the SO2 emissions shifted to the northeast, from Zhumadian City to Zhoukou City in Henan Province. The results of hot spots showed a polarization trend of “clustering hot spots in the north and dispersing cold spots in the south”. (3) The MGWR model is more accurate than the OLS and classical GWR regressions. The different spatial bandwidths have a different effect on the identification of influencing factors. There were several main influencing factors on urban SO2 emissions: the regional innovation and entrepreneurship level, government intervention, and urban precipitation; important factors: population intensity, financial development, and foreign direct investment; secondary factors: industrial structure upgrading and road construction. Based on the above conclusions, this paper explores the spatial heterogeneity of urban SO2 emissions and their influencing factors, and provides empirical evidence and reference for the precise management of SO2 emission reduction in “one city, one policy”.

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“…We explored LST at the district level, which not only facilitates the formulation and implementation of heat island effect mitigation strategies [8,39], but also enriches our driving factors with data from the statistics department, such as regional fossil energy consumption. Beijing, the capital city of China, is a center of human activities and abundant energy fuels have been consumed [66,67]. Anthropogenic heat is a prominent factor influencing the local thermal condition [7], so fossil energy consumption should be considered when exploring the driving mechanism of LST effectively.…”

Section: A Driving Factor Of Lstmentioning

confidence: 99%

Understanding the Drivers of Land Surface Temperature Based on Multisource Data: A Spatial Econometric Perspective

Liu¹,

Ma²,

Bai³

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Unveiling the driving factors of carbon emissions from industrial resource allocation in China: A spatial econometric perspective

Cited by 83 publications

References 37 publications

The Impact of Green Technology Innovation on Carbon Emissions in the Context of Carbon Neutrality in China: Evidence from Spatial Spillover and Nonlinear Effect Analysis

The Impact of Green Technology Innovation on Carbon Emissions in the Context of Carbon Neutrality in China: Evidence from Spatial Spillover and Nonlinear Effect Analysis

Spatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO2 Emissions in Chinese Cities: Fresh Evidence from MGWR

Understanding the Drivers of Land Surface Temperature Based on Multisource Data: A Spatial Econometric Perspective

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