Embodied carbon emissions induced by the construction of hydropower infrastructure in China

Ge, Zewen; Geng, Yong; Wei, Wendong; Jiang, Mingkun; Chen, Bin; Li, Jiashuo

doi:10.1016/j.enpol.2022.113404

Cited by 14 publications

(4 citation statements)

References 48 publications

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“…At the same time, the initial construction may require corresponding supporting infrastructure, which allows many energy-intensive products to be consumed, thus increasing carbon emissions. This is consistent with the current view of related research [ 59 ]. When GI reaches a certain level, the demand for a high-quality environment and green development will increase, and the efficiency of production capacity and equipment will be enhanced by technological advancement [ 60 ].…”

Section: Resultssupporting

confidence: 94%

Does green investment reduce carbon emissions? New evidence from partially linear functional-coefficient models

Huang

2023

Heliyon

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Section: Resultssupporting

confidence: 94%

Does green investment reduce carbon emissions? New evidence from partially linear functional-coefficient models

Huang

2023

Heliyon

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“…Many scholars explore the impact of embodied carbon emissions from the scale effect [32,33], structural effect [34,35], and intensity influence of products [36,37]. Accordingly, this paper mainly explores the impact of embodied carbon emissions from three aspects: the scale of China-Russia agricultural trade, trade structure, and technological progress.…”

Section: Decomposition Analysis Of Implicit Carbon Emission Influenci...mentioning

confidence: 99%

Study on Measurement and Influencing Factors of Trade Embodied Carbon-Based on China-Russia Agricultural Products Trade

Wei,

Li,

et al. 2024

Pol. J. Environ. Stud.

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China and Russia are major agricultural countries with abundant agricultural resources, holding important global agricultural trade market positions. While the scale of China-Russia agricultural trade has maintained rapid growth, the embodied carbon emissions from agricultural products have also increased. This article first uses input-output data to construct a multi-regional input-output model under a total trade accounting method to calculate the carbon emission coefficients, trade value-added, and embodied carbon emissions of three categories of agricultural products traded between China and Russia from 2009 to 2019. Our results show that, from an industry perspective, Category C3 agricultural products have the highest carbon emission coefficients; Category C1 agricultural products have the highest export value-added. From the standpoint of imports and exports, Russia's export value-added to China has proliferated in recent years and is slightly higher than that of Chinese exports to Russia; the embodied carbon emissions from Chinese agricultural products are higher than those from Chinese exports to Russia. We further examined the impact of trade scale, trade structure, and carbon emission intensity on embodied carbon, finding that these three factors have heterogeneous effects on the embodied carbon of agricultural trade for different industries and countries.

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“…China has the world's largest installed capacity of hydropower, which contributes over 60% of the country's renewable energy supply (Ge et al, 2023). Hydroelectric power, as a clean energy source, can reduce carbon emissions while meeting energy needs (Li and Zhang, 2014).…”

Section: Introductionmentioning

confidence: 99%

Refined machine learning modeling of reservoir discharge water temperature

Huang,

Chen

2024

Front. Environ. Sci.

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Water temperature is a controlling factor for physical, biological, and chemical processes in rivers, and is closely related to hydrological factors. The construction of reservoirs interferes with natural water temperature fluctuations. Hence constructing a model to accurately and efficiently predict the reservoir discharge water temperature (DWT) is helpful for the protection of river water ecology. Although there have been studies on constructing efficient and accurate machine learning prediction models for DWT, to our knowledge, there is currently no research focused on hourly scales. The study proposed in this paper is based on high-frequency monitoring data of vertical water temperature in front of a dam, water level, discharge flow, and DWT. In this study, six types of machine learning algorithms, namely, support vector regression, linear regression, k-nearest neighbor, random forest regressor, gradient boosting regression tree, and multilayer perceptron neural network, were used to construct a refined prediction model for DWT. The results indicated that the SVR model using the radial basis function as the kernel function had the best modeling performance. Based on the SVR model, we constructed a 1–24 h early warning model and optimized the scheduling of DWT based on changing discharge flow. In summary, a machine learning model for DWT that can provide short-term forecasting and decision support for reservoir managers was refined in this study.

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Embodied carbon emissions induced by the construction of hydropower infrastructure in China

Cited by 14 publications

References 48 publications

Does green investment reduce carbon emissions? New evidence from partially linear functional-coefficient models

Does green investment reduce carbon emissions? New evidence from partially linear functional-coefficient models

Study on Measurement and Influencing Factors of Trade Embodied Carbon-Based on China-Russia Agricultural Products Trade

Refined machine learning modeling of reservoir discharge water temperature

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