Spatio-temporal dynamics and determinants of new energy policy diffusion in China: A policy citation approach

Ba, Zhichao; Zhao, Yuxiang; Liu, Xuetai; Li, Gang

doi:10.1016/j.jclepro.2022.134270

Cited by 14 publications

(4 citation statements)

References 47 publications

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“…This transition helps to reduce dependence on fossil fuels, lower CE, and increase CP. (5) The regions of the Northwest, Southwest, South Coast, Middle Yellow River, and the Middle Reaches of the Yangtze River have higher LFI. This attracts advanced technology and management experience, promoting technology transfer and knowledge spillover.…”

Section: Heterogeneity Analysis Resultsmentioning

confidence: 96%

“…Due to the variety and complexity of the causes of economic change, it is difficult to do an analysis of all the influencing variables. In order to better express the pass-through effect of omitted variables and economic error terms on Y, the concept of residual effect is introduced to explain it, and its expression is shown in Equation (5).…”

Section: Methods Of Pathway Analysismentioning

confidence: 99%

“…Technological innovation, as the fundamental driving force, can promote the large-scale and leapfrog development of renewable energy in our country. China has successively supported and guided the development of renewable energy technologies through financial and fiscal policies, standard setting, and international cooperation [5]. As an emerging branch within the field of technological innovation research, Renewable Energy Technology Innovation (REIT) has not yet reached a consensus among scholars regarding its categorization.…”

Section: Introductionmentioning

confidence: 99%

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Impact Mechanism of Renewable Energy Technology Innovation on Carbon Productivity Based on Spatial Durbin Model

Wang,

Chen

2024

Sustainability

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Given the threats to international energy security and the restructuring of energy sources in various countries, China faces the dual challenge of achieving the goals of carbon peaking and carbon neutrality. To promote a reduction in carbon emissions and enhance carbon productivity, it is crucial to innovate renewable energy technology for long-term, low-carbon transformational development. This paper identifies the key factors that affect carbon productivity through pathway analysis and quantitatively examines the direct and spatial spillover impacts of technological advancements in renewable energy using the spatial Durbin model. Finally, this study verifies the pathways through which innovations affect carbon productivity by combining them with the spatial mediation model. The results indicate that innovations in renewable energy technologies significantly enhance carbon productivity. The indirect effects of regional spillover are even more pronounced.

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Section: Heterogeneity Analysis Resultsmentioning

confidence: 96%

Section: Methods Of Pathway Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact Mechanism of Renewable Energy Technology Innovation on Carbon Productivity Based on Spatial Durbin Model

Wang,

Chen

2024

Sustainability

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“…We will provide a helpful perspective showing the degree of match between various remediation technologies and specific pollutants. Moreover, spatial autocorrelation analysis, a critical tool in soil remediation, is used in various applications such as priority setting, health risk assessment, and source prevention and control [20][21][22]. However, in the context of remediation technologies for contaminated sites, spatial factors are often overlooked despite their importance.…”

Section: Introductionmentioning

confidence: 99%

Remediation Technologies of Contaminated Sites in China: Application and Spatial Clustering Characteristics

Yu,

Wang,

Yuan

et al. 2024

Sustainability

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Screening remediation technologies through the lens of green, low-carbon, and sustainable development is crucial for contaminated land management. To better understand the applicability of remediation technologies, this paper explored their application in China based on a survey of 643 cases. By employing coupled analysis and local spatial autocorrelation methods, this study reveals the alignment between remediation technologies and pollutants, along with their spatial distribution and clustering patterns. Specifically, the four primary remediation technologies identified were cement kiln co-processing (CKCP), chemical oxidation/reduction (CO/CR), thermal desorption (TR), and solidification and stabilization (S/S), collectively accounting for over 90% of the cases. Additionally, our findings indicated significant variation in how different pollutants respond to remediation technologies, largely attributable to the characteristics of the pollutants. We observed High–High clustering patterns for CKCP, CO/CR, TR, and S/S. These were predominantly found in Jiangsu, Chongqing, Shandong, and Guizhou for CKCP and CO/CR and in Hebei, Jiangsu, Shanghai, and Chongqing for CO/CR. TR exhibited a High–High clustering in Shanghai, as did S/S. This research contributes to reducing the economic and resource costs associated with the trial-and-error of screening contaminated soil remediation technologies, offering valuable scientific and technological guidance for contaminated land regulation.

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Economic level, environmental regulation, and new energy industry development

Xiang,

Wang

2024

Environ Sci Pollut Res

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Spatio-temporal dynamics and determinants of new energy policy diffusion in China: A policy citation approach

Cited by 14 publications

References 47 publications

Impact Mechanism of Renewable Energy Technology Innovation on Carbon Productivity Based on Spatial Durbin Model

Impact Mechanism of Renewable Energy Technology Innovation on Carbon Productivity Based on Spatial Durbin Model

Remediation Technologies of Contaminated Sites in China: Application and Spatial Clustering Characteristics

Economic level, environmental regulation, and new energy industry development

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