Driving factors of total carbon emissions from the construction industry in Jiangsu Province, China

Li, Dezhi; Huang, Gan; Zhang, Kevin; Wang, Jiangbo

doi:10.1016/j.jclepro.2020.123179

Cited by 82 publications

(22 citation statements)

References 76 publications

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“…Scholars often use carbon emission intensity, carbon dioxide emissions index, or carbon productivity to characterize the carbon emission efficiency. Here, the carbon dioxide emission intensity is the ratio of carbon dioxide emissions to the per unit of GDP, some scholars insisted that it is an ideal indicator to calculate the carbon dioxide emission efficiency [5,6]. Carbon dioxide emissions index refers to the carbon dioxide emissions per unit of energy consumption [7].…”

Section: E Definition Of Carbon Dioxide Emission Efficiencymentioning

confidence: 99%

Regional Variation in the Carbon Dioxide Emission Efficiency of Construction Industry in China: Based on the Three-Stage DEA Model

Wei-zhong

2021

Discrete Dynamics in Nature and Society

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China’s construction industry emits a huge amount of carbon dioxide with the extensive and rapid development, and the carbon dioxide emissions from construction sector have become a social problem. This article evaluates the carbon dioxide emission from China’s regional construction industry by the three-stage DEA method. The research results indicate that the environmental factors and random error seriously affect the carbon dioxide emission efficiency of construction industry. Eliminated the above factors, the mean of the carbon dioxide emission efficiency has the tendency of rising firstly and then decreasing, which increased from 0.67 in 2010 to 0.76 in 2014 and then decreased to 0.66 in 2019 in the third stage. On the whole, China’s regional construction industry has the low carbon dioxide emission efficiency. In the regions, the Eastern region has the highest technology efficiency value among the three regions, followed by the Middle region and the West region. Therefore, the key method to increase the carbon emission efficiency of construction industry is to improve the management efficiency and change the growth mode.

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Section: E Definition Of Carbon Dioxide Emission Efficiencymentioning

confidence: 99%

Regional Variation in the Carbon Dioxide Emission Efficiency of Construction Industry in China: Based on the Three-Stage DEA Model

Wei-zhong

2021

Discrete Dynamics in Nature and Society

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“…For instance, Bandyopadhyay et al used the gate circulation network and the LSTM model to predict and estimate the number of COVID-19 diagnosed, dead and cured cases 13 . And Huang et al used the deep learning method based on the convolutional neural network to predict the cumulative number of deaths of COVID-19 14 . Zang et al 15 demonstrated that CNN–LSTM, LSTM, and CNN models were more accurate than ANN and SVM models in the short-term forecasting of global horizontal irradiance (GHI).…”

Section: Introductionmentioning

confidence: 99%

The prediction and analysis of COVID-19 epidemic trend by combining LSTM and Markov method

Zheng

Wang

et al. 2021

Sci Rep

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Corona Virus Disease 2019 (COVID-19) has spread rapidly to countries all around the world from the end of 2019, which caused a great impact on global health and has had a huge impact on many countries. Since there is still no effective treatment, it is essential to making effective predictions for relevant departments to make responses and arrangements in advance. Under the limited data, the prediction error of LSTM model will increase over time, and its prone to big bias for medium- and long-term prediction. To overcome this problem, our study proposed a LSTM-Markov model, which uses Markov model to reduce the prediction error of LSTM model. Based on confirmed case data in the US, Britain, Brazil and Russia, we calculated the training errors of LSTM and constructed the probability transfer matrix of the Markov model by the errors. And finally, the prediction results were obtained by combining the output data of LSTM model with the prediction errors of Markov Model. The results show that: compared with the prediction results of the classical LSTM model, the average prediction error of LSTM-Markov is reduced by more than 75%, and the RMSE is reduced by more than 60%, the mean $${R}^{2}$$ R 2 of LSTM-Markov is over 0.96. All those indicators demonstrate that the prediction accuracy of proposed LSTM-Markov model is higher than that of the LSTM model to reach more accurate prediction of COVID-19.

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“…Thus, to accomplish the above ambitious targets of emission reduction requires finding efficient ways to control CO 2 emissions. The provinces of China are special administrative divisions connecting the nation as a whole and the cities, and so they play an important role in realizing the reduction target of China’s CO 2 emissions [ 8 ]. It stands to reason that it is crucial for us to analyze the influencing factors of CO 2 emissions and accelerate the decoupling process between CO 2 emissions and economic growth in Gansu province.…”

Section: Introductionmentioning

confidence: 99%

Decomposition and Decoupling Analysis of CO2 Emissions Based on LMDI and Two-Dimensional Decoupling Model in Gansu Province, China

Xin

Jia

et al. 2021

IJERPH

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Currently, little attention has been paid to reducing carbon dioxide (CO2) emissions of Gansu, and the two-dimensional decoupling model has been rarely used to study the relationship between the economic development and CO2 emissions, especially in western China (e.g., Gansu). Thus, here, we first used the Logarithmic Mean Divisia Index (LMDI) to decompose the driving factors of Gansu’s CO2 emissions between 2000–2017 and then analyzed the decoupling relationship by using the two-dimensional model. Results showed: (1) Gansu’s CO2 emissions increased from 7805.70 × 104 t in 2000 to 19,896.05 × 104 t in 2017. The secondary industry accounted for the largest proportion in Gansu’s CO2 emissions, followed by the tertiary industry and the primary industry. (2) The economic output showed the dominant driving effect on Gansu’s CO2 emissions growth with the cumulative contribution rate of 201.94%, followed by the effects of industrial structure, population size, and energy structure, and their cumulative contribution rates were 9.68%, 7.81%, and 3.05%, respectively. In contrast, the energy intensity effect presented the most obvious mitigating effect with the cumulative contribution rate of −122.49%. (3) The Environmental Kuznets Curve (EKC) between CO2 emissions and economic growth was demonstrated the inverted U-shape in Gansu. The two-dimensional decoupling status was the low level-weak decoupling (WD-LE) during 2000–2017. Thus, dropping the proportion of the secondary industry, reducing the use of carbon-intensive fuel like coal, introducing advanced technologies, and increasing the investment of new energy might effectively restrain the growth of Gansu’s CO2 emissions.

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Driving factors of total carbon emissions from the construction industry in Jiangsu Province, China

Cited by 82 publications

References 76 publications

Regional Variation in the Carbon Dioxide Emission Efficiency of Construction Industry in China: Based on the Three-Stage DEA Model

Regional Variation in the Carbon Dioxide Emission Efficiency of Construction Industry in China: Based on the Three-Stage DEA Model

The prediction and analysis of COVID-19 epidemic trend by combining LSTM and Markov method

Decomposition and Decoupling Analysis of CO2 Emissions Based on LMDI and Two-Dimensional Decoupling Model in Gansu Province, China

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