Carbon emission rush in response to the carbon reduction policy in China

Qi, Rui; Shi, Chenchen; Wang, Mark

doi:10.1177/0920203x221093188

Cited by 6 publications

(6 citation statements)

References 53 publications

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“…For each time stamp, the energy consumed by the machining systems consists of energy-consuming components, such as spindles, feed axes, lighting units, hydraulic lubrication units, and workpiece characteristics, and process parameters. Different energy-consuming component units contain a variety of processing parameters, mainly including spindle speed, feed rate, cutting depth, number of cutting edges, tool diameter, etc., which can be expressed by Equation (1).…”

Section: Ec Characteristics Of Machining Systemsmentioning

confidence: 99%

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An Integrated Deep-Learning-Based Approach for Energy Consumption Prediction of Machining Systems

Zhang

Yan

et al. 2023

Sustainability

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Large and extensive manufacturing systems consume a large proportion of manufacturing energy. A key component of energy efficiency management is the accurate prediction of energy efficiency. However, the nonlinear and vibration characteristics of machining systems’ energy consumption (EC) pose a challenge to the accurate prediction of system EC. To address this challenge, an energy consumption prediction method for machining systems is presented, which is based on an improved particle swarm optimization (IPSO) algorithm to optimize long short-term memory (LSTM) neural networks. The proposed method optimizes the LSTM hyperparameters by improving the particle swarm algorithm with dynamic inertia weights (DIWPSO-LSTM), which enhances the prediction accuracy and efficiency of the model. In the experimental results, we compared several improved optimization algorithms, and the proposed method has a performance improvement of more than 30% in mean absolute error (MAE)and mean error(ME).

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Section: Ec Characteristics Of Machining Systemsmentioning

confidence: 99%

“…Energy and environmental issues have received extensive attention in recent years [1][2][3]. Due to the wide use of machine tools, machining systems are considered to be the largest energy consumer in the manufacturing industry [4,5].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Deep-Learning-Based Approach for Energy Consumption Prediction of Machining Systems

Zhang

Yan

et al. 2023

Sustainability

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“…It is not appropriate for policymakers to treat different stages of development or different sectors as the same situation when they are thinking about energy transition policies. Otherwise, the 'carbon rush' would Qi, Shi [42].…”

Section: Policy Implication and Conclusionmentioning

confidence: 99%

Energy Transition and Economic Development in China: A National and Sectorial Analysis from a New Structural Economics Perspectives

et al. 2022

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New Structural Economics (NSE) predicts that structural change in energy production would follow different patterns during different development stages and across different sectors. These variations require a range of policy responses. In this paper, we investigate this assertion by modeling China’s energy transition and economic development based on provincial panel data from 2000 to 2012. By using static models (Fama–MacBeth, OLS, fixed effect) and dynamic models (difference and system GMM), we find the relationship between low-carbon energy transition and economic development presents a U-shaped curve at the national level, but it is an inverted-U curve at the residential level. Furthermore, it is ambiguous in the agricultural sector and independent of economic development in the industry and service sectors. Institutional factors, natural resource endowment, environmental policy, and technological change influence China’s energy transition. Our findings supports NSE application in the Chinese energy economy and diversify energy transition policy by adjusting to the local conditions.

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“…Introducing the dual carbon target reflects China's recognition of the urgency of combating climate change and mitigating environmental impacts [6]. The dual carbon target underscores China's status as a significant global emitter while highlighting its pivotal role in global efforts to combat climate change [7]. By committing to reduce carbon emissions and ultimately becoming carbon neu-tral, China has set an exemplary precedent for other countries to emulate [8].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Evolution and Future of Carbon Storage in Resource-Based Chinese Province: A Case Study from Shanxi Using PLUS–InVEST Model Prediction

Jiao,

Wang,

et al. 2024

Sustainability

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Resource exploitation markedly alters land use and ecological carbon storage, posing risks to carbon sinks and food security. This study analyzes land-use change from 1990 to 2020 in the resource-based province of Shanxi, China. By introducing a mineral resource driver, the PLUS model was used to predict four scenarios: natural development (ND), cropland protection (CP), ecological protection (EP), and dual protection of ecology and cropland (DP). The spatial and temporal evolutions of carbon storage were then analyzed using the InVEST model. Forests were predominantly distributed in mountainous areas, with croplands in southerly and central flat areas, construction lands in and around cities, and mining lands sporadically distributed across Shanxi. From 1990 to 2020, croplands and grasslands decreased, while forest, construction, and mining lands increased. Carbon storage decreased continuously, with a total loss of 15.1 × 106 t. High-value carbon storage areas were in the Lüliang, Taihang, and Taiyue Mountains, and low-value areas were in the more populous central and southern regions. Carbon storage was predicted to decline by 2035 under the ND and CP scenarios and to exceed that of 2020 under the EP and DP scenarios. The DP scenario projected an increase of 4.93 × 106 t in carbon storage by 2035. The DP scenario realizes the protection of carbon sinks in resource-based areas and maintains food security, providing a theoretical reference for achieving carbon neutrality and high-quality sustainable development in Shanxi Province.

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Carbon emission rush in response to the carbon reduction policy in China

Cited by 6 publications

References 53 publications

An Integrated Deep-Learning-Based Approach for Energy Consumption Prediction of Machining Systems

An Integrated Deep-Learning-Based Approach for Energy Consumption Prediction of Machining Systems

Energy Transition and Economic Development in China: A National and Sectorial Analysis from a New Structural Economics Perspectives

Spatiotemporal Evolution and Future of Carbon Storage in Resource-Based Chinese Province: A Case Study from Shanxi Using PLUS–InVEST Model Prediction

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