Understanding the structure and determinants of intercity carbon emissions association network in China

Cai, Hechang; Wang, Zilong; Zhu, Yongfeng

doi:10.1016/j.jclepro.2022.131535

Cited by 21 publications

(4 citation statements)

References 59 publications

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“…The gravitational model is based on Newton's law of universal gravitation to measure the spatial interaction of a certain element type between two regions, which has been widely used in economics, trade, and finance to portray spatial interaction capabilities. Previously, many scholars have used gravitational model to analyze the spatial correlation of regional carbon emissions (Cai et al, 2022; Wang et al, 2018). The quantitative calculation formula of the correlation intensity between the two regions is as follows.

I_{italicij} = k_{italicij} \frac{M_{i} M_{j}}{D_{ij}^{b}},

where

I_{italicij}

is the correlation strength between region

i

and region

j

;

k_{italicij}

is the empirical weight;

M_{i}

and

M_{j}

are the factor quantities of region

i

and region

j

, respectively;

D_{italicij}

is the distance between the two cities; b is the set coefficient.…”

Section: Methodsmentioning

confidence: 99%

“…The gravitational model is based on Newton's law of universal gravitation to measure the spatial interaction of a certain element type between two regions, which has been widely used in economics, trade, and finance to portray spatial interaction capabilities. Previously, many scholars have used gravitational model to analyze the spatial correlation of regional carbon emissions (Cai et al, 2022;Wang et al, 2018). The quantitative calculation formula of the correlation intensity between the two regions is as follows.…”

Section: Carbon Emission Gravitational Modelmentioning

confidence: 99%

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Land use data can improve the accuracy of carbon emission spatial inversion model

Wang,

Yang,

Bai

et al. 2024

Land Degrad Dev

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Investigating the spatial distribution and correlation characteristics of carbon emissions would be conducive to the policy formulation for precise carbon emission spatial reduction. Firstly, a new carbon emission spatial inversion model was developed, incorporating nighttime light data and land use data. After verifying the validity and accuracy of the inversion results, the continuous carbon emission spatial data in the Beijing‐Tianjin‐Hebei Urban Agglomeration (BTHUA) were acquired from 2000 to 2019. Then, the spatial distribution and correlation characteristics were further analyzed in the BTHUA. Finally, policy recommendations were proposed for carbon emission reduction and urban sustainable development. The results showed that the built model can improve the accuracy of the carbon emission spatial inversion data. The carbon emissions were low in the northwest and high in the southeast of the BTHUA, with a noticeable expansion of the high carbon emission contiguous areas around Beijing, Tianjin, Shijiazhuang, and other prefecture‐level cities, which was consistent with the socioeconomic development pattern. The center of gravity of carbon emissions moved to the southeast, showing a relatively stable distribution. The spatial correlation degree of carbon emissions among cities gradually increased, with Beijing and Tianjin playing a prominent role. As a scientific tool, the spatial inversion model helps to produce more accurate spatial data. The results and conclusions can provide useful and scientific references for spatial analysis and regulation strategies of regional carbon emission reduction.

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I_{italicij} = k_{italicij} \frac{M_{i} M_{j}}{D_{ij}^{b}},

where

I_{italicij}

is the correlation strength between region

i

and region

j

;

k_{italicij}

is the empirical weight;

M_{i}

and

M_{j}

are the factor quantities of region

i

and region

j

, respectively;

D_{italicij}

is the distance between the two cities; b is the set coefficient.…”

Section: Methodsmentioning

confidence: 99%

“…The gravitational model is based on Newton's law of universal gravitation to measure the spatial interaction of a certain element type between two regions, which has been widely used in economics, trade, and finance to portray spatial interaction capabilities. Previously, many scholars have used gravitational model to analyze the spatial correlation of regional carbon emissions (Cai et al, 2022;Wang et al, 2018). The quantitative calculation formula of the correlation intensity between the two regions is as follows.…”

Section: Carbon Emission Gravitational Modelmentioning

confidence: 99%

Land use data can improve the accuracy of carbon emission spatial inversion model

Wang,

Yang,

Bai

et al. 2024

Land Degrad Dev

View full text Add to dashboard Cite

show abstract

“…It has many advantages, such as being able to analyze the endogenous mechanism of network evolution, include time trends, and analyze the dynamic evolution mechanism of the network from a more comprehensive perspective. It has been widely used in recent years to explore the driving mechanisms of spatial correlation networks [58]. This paper constructs the TERGM of the spatial correlation network driving mechanisms of transportation carbon emission intensity based on the assumption that the time interval is one year.…”

Section: Construction Of the Tergmmentioning

confidence: 99%

Analysis of the Spatial Correlation Network and Driving Mechanism of China’s Transportation Carbon Emission Intensity

Yuan,

Zhu,

Zhang

et al. 2024

Sustainability

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From 2008 to 2021, this study analyzed the spatial correlation characteristics between provincial transportation carbon emission intensity and explored ways to reduce transportation carbon emissions. This study used the modified gravity model, social network analysis (SNA) method, and temporal exponential random graph model (TERGM) to analyze the spatial correlation network evolution characteristics and driving mechanism of China’s transportation carbon emission intensity. This study found that China’s transportation carbon emission intensity and spatial correlation network have unbalanced characteristics. The spatial correlation network of transportation carbon emission intensity revealed that Shanghai, Beijing, Tianjin, Guangdong, Fujian, and other provinces were at the center of the network, with significant intermediary effects. The spatial correlation of transportation carbon emission intensity was divided into four functional plates: “two-way spillover”, “net benefit”, “broker”, and “net spillover”. The “net benefit” plate was mainly located in developed regions, and the “net spillover” plate was primarily located in underdeveloped regions. Endogenous structural and exogenous mechanism variables were the main factors affecting the evolution of the spatial correlation network of provincial transportation carbon emission intensity.

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“…Gao [4] constructed a non-competitive input-output model to measure the embodied carbon emissions of 28 industry sectors in China from 2005 to 2017. In 2022, Cai [5] used carbon emissions data to map inter-city linkages and build networks that have substantial implications for China's inter-city carbon emission linkage network.…”

Section: Related Workmentioning

confidence: 99%

Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory

Chen,

et al. 2023

Processes

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Carbon emissions play a significant role in shaping social policy-making, industrial planning, and other critical areas. Recurrent neural networks (RNNs) serve as the major choice for carbon emission prediction. However, year-frequency carbon emission data always results in overfitting during RNN training. To address this issue, we propose a novel model that combines oscillatory particle swarm optimization (OPSO) with long short-term memory (LSTM). OPSO is employed to fine-tune the hyperparameters of LSTM, utilizing an oscillatory strategy to effectively mitigate overfitting and consequently improve the accuracy of the LSTM model. In validation tests, real data from Hainan Province, encompassing diverse dimensions such as gross domestic product, forest area, and ten other relevant factors, are used. Standard LSTM and PSO-LSTM are selected in the control group. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are used to evaluate the performance of these methods. In the test dataset, the MAE of OPSO-LSTM is 117.708, 65.72% better than LSTM and 29.48% better than PSO-LSTM. The RMSE of OPSO-LSTM is 149.939, 68.52% better than LSTM and 41.90% better than PSO-LSTM. The MAPE of OPSO-LSTM is 0.017, 65.31% better than LSTM, 29.17% better than PSO-LSTM. The experimental results prove that OPSO-LSTM can provide reliable predictions for carbon emissions.

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Understanding the structure and determinants of intercity carbon emissions association network in China

Cited by 21 publications

References 59 publications

Land use data can improve the accuracy of carbon emission spatial inversion model

Land use data can improve the accuracy of carbon emission spatial inversion model

Analysis of the Spatial Correlation Network and Driving Mechanism of China’s Transportation Carbon Emission Intensity

Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory

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