Equilibrating provincial carbon increments for residential buildings in China under carbon peaking constraints

Chen, Liu; Li, Rui; Zhang, Jinbo; Cai, Weiguang; Zhang, Kaiqiang; Sun, Yida

doi:10.1016/j.eiar.2023.107385

Cited by 7 publications

(1 citation statement)

References 57 publications

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“…Subsequently, the scope of scholarly research has broadened to encompass various sectors, including transportation [36], logistics [37], manufacturing [38], mining [39], and additional fields.…”

Section: Introductionmentioning

confidence: 99%

The Influencing Factors of Carbon Emissions in the Industrial Sector: Empirical Analysis Based on a Spatial Econometric Model

Xie,

Lu,

Xie

2024

Sustainability

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To promote the low-carbon, high-quality development of China’s industrial sector and achieve the national carbon peak goal as soon as possible, this study explores the influencing factors of carbon emissions among industrial sectors. Based on the panel data of 36 industrial sectors in China from 2009 to 2021, the spatial effects and characteristics of industrial sectors are examined by the spatial Durbin model (SDM) based on analyzing the spatial correlation among industrial sectors. The results show the following: (1) Moran’s I statistical results show that China’s industrial carbon emissions have a strong positive spatial correlation, and with time, the spatial correlation between industrial sectors gradually increases. (2) The empirical results of the whole industrial sector show that the property rights structure, capital intensity, and energy structure are the main driving forces promoting carbon emission reduction; the grouping analysis results show that the impact of FDI and property rights structure on the carbon emissions of the industrial sector in different sample groups is different. Among them, the energy structure and research and development play a role in reducing carbon emissions in each sample group. (3) Therefore, in the future, to reduce carbon emissions in the industrial sector, it is necessary to inhibit growth factors and promote the role of reduction factors; optimizing the energy structure and improving the rationality of the property rights structure are effective ways to achieve energy conservation and emission reduction.

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Section: Introductionmentioning

confidence: 99%

The Influencing Factors of Carbon Emissions in the Industrial Sector: Empirical Analysis Based on a Spatial Econometric Model

Xie,

Lu,

Xie

2024

Sustainability

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The carbon emissions calculation, decomposition and peak scenario simulation of meat, egg, and milk in China: from a production and consumption perspective

Zhou,

Chen,

et al. 2024

Clean Techn Environ Policy

Self Cite

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Characterizing and quantifying livestock carbon emissions helps mitigate temperature rise and potential climate risks. Here, the spatial and temporal characteristics of China's carbon emissions of meat, egg, and milk are analyzed from the perspective of production and consumption. A driving factor decomposition model of carbon emissions of meat, egg, and milk production and consumption is established using the logarithmic mean Divisia index (LMDI). Using a static scenario analysis and dynamic Monte Carlo simulations (MCS), the evolution trajectory of carbon peak in meat, egg, and milk production and consumption in China during 2021-2030 is analyzed. China's carbon emissions from meat, egg, and milk production and consumption have risen in 2020, reaching 794.1 and 465.88 Mt, respectively. Moreover, western China has the highest carbon emissions in production (33.84%). For consumption, the carbon emissions are mainly concentrated in the eastern region (42.96%). The key drivers of carbon emissions for this production and consumption are the farmland value and residential income effects, respectively. Conversely, reverse urbanization, agricultural industry structure, and food money purchasing power effects play significant suppressive roles. Under the business-as-usual scenario (BAU), China's carbon emissions from meat, egg, and milk production and consumption in 2030 will increase by 40.1% and 4.86% from 2020, respectively. The middle low-carbon scenario (Mid-LC) mitigates carbon emission growth by 24.51% and −5.7%, respectively, for the production and consumption of meat, egg, and milk in 2030 compared to 2020. In the highly low-carbon scenario (Hig-LC), production and consumption will achieve a carbon peak by 2030. Ultimately, this study highlights the importance of reducing carbon emissions from livestock on production and consumption, providing a new perspective for green development in China and other emerging economies.

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Differences in energy-saving benefits between the rich and poor: data-driven method to study equity of mandatory energy efficiency policy in Chongqing

Chen,

Ran,

Wang

et al. 2024

Environ Sci Pollut Res

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Equilibrating provincial carbon increments for residential buildings in China under carbon peaking constraints

Cited by 7 publications

References 57 publications

The Influencing Factors of Carbon Emissions in the Industrial Sector: Empirical Analysis Based on a Spatial Econometric Model

The Influencing Factors of Carbon Emissions in the Industrial Sector: Empirical Analysis Based on a Spatial Econometric Model

The carbon emissions calculation, decomposition and peak scenario simulation of meat, egg, and milk in China: from a production and consumption perspective

Differences in energy-saving benefits between the rich and poor: data-driven method to study equity of mandatory energy efficiency policy in Chongqing

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