Forecast of urban traffic carbon emission and analysis of influencing factors

Li, Yanmei; Li, Tingting; Lu, Shuangshuang

doi:10.1007/s12053-021-10001-0

Cited by 35 publications

(17 citation statements)

References 49 publications

(39 reference statements)

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“…Alam and AlArjani (2021) selected the ARIMA forecasting models to anticipate annual carbon emissions in the Gulf countries. Li et al (2021) proposed an improved SARIMA to reproduce time series with distinct seasonal carbon cycle and climate change indexes for reliable estimates to better inform the decision-making for sustainable environmental management. Arcos-Vargas et al (2023) proposed the estimation of an ARIMA-SARIMA model that looks for the main determinants of the variations in hourly energy prices and carbon emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Alam and AlArjani (2021) selected the ARIMA forecasting models to anticipate annual carbon emissions in the Gulf countries. Li et al . (2021) proposed an improved SARIMA to reproduce time series with distinct seasonal carbon cycle and climate change indexes for reliable estimates to better inform the decision-making for sustainable environmental management.…”

Section: Literature Reviewmentioning

confidence: 99%

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Forecasting Chinese carbon emission intensity based on the interactive effect GM(1,N) power model

Wang,

2023

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PurposeThis study aims to predict China's carbon emission intensity and put forward a set of policy recommendations for further development of a low-carbon economy in China.Design/methodology/approachIn this paper, the Interaction Effect Grey Power Model of N Variables (IEGPM(1,N)) is developed, and the Dragonfly algorithm (DA) is used to select the best power index for the model. Specific model construction methods and rigorous mathematical proofs are given. In order to verify the applicability and validity, this paper compares the model with the traditional grey model and simulates the carbon emission intensity of China from 2014 to 2021. In addition, the new model is used to predict the carbon emission intensity of China from 2022 to 2025, which can provide a reference for the 14th Five-Year Plan to develop a scientific emission reduction path.FindingsThe results show that if the Chinese government does not take effective policy measures in the future, carbon emission intensity will not achieve the set goals. The IEGPM(1,N) model also provides reliable results and works well in simulation and prediction.Originality/valueThe paper considers the nonlinear and interactive effect of input variables in the system's behavior and proposes an improved grey multivariable model, which fills the gap in previous studies.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Chinese carbon emission intensity based on the interactive effect GM(1,N) power model

Wang,

2023

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“…Summarizing the current research on China's CE peak prediction and total amount control, it can be found that scholars mainly analyze the influencing factors of total CEs through the following methods (Chai et al, 2017;Dong et al, 2019), namely, the STIRPAT model, the logarithmic mean Divisia index (LMDI) factor decomposition model, the environmental Kuznets curve (EKC) curve, and other means, that were established to decompose CEs of energy consumption so as to put forward corresponding countermeasures and suggestions for China to eliminate backward production capacity and accelerate the upgrading of the industrial structure. For the prediction of total CEs, most of the existing studies used the partial least squares regression method, the STIRPAT model, and the scenario analysis method to predict the peak of China's CE, and some scholars combined various methods to predict China's CEs (Yang et al, 2018;Li et al, 2021). Jin et al used the radial basis function (RBF) to predict the urban CE content in China from 2027 to 2032 (Jin, 2021).…”

Section: Prediction Of Cementioning

confidence: 99%

Application of carbon emission prediction based on a combined neural algorithm in the control of coastal environmental pollution in China

Wang

2022

Front. Ecol. Evol.

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The marine ecosystem provides the environment, resources, and services necessary for the development of every human society. In recent years, China's coastal zone has been polluted to varying degrees, which has seriously affected its development. The characteristics of marine environmental data include the variety of data types, the complexity of factors affecting the marine environment, and the unpredictability of marine pollution. Currently, there are few studies applying the clustering analysis algorithm to marine environmental monitoring. Then, carbon emissions (CEs) from coastal areas are predicted using marine environmental data. Therefore, this paper mainly studies the spatial and temporal accumulation characteristics of marine environmental data and uses the fuzzy c-means (FCM) algorithm to mine the data monitored by the marine environment. Meanwhile, it has been focused on the prediction of coastal CEs, and the grey model-back propagation (GM-BP) algorithm has been developed to predict CEs from coastal areas, which solves the problem that the traditional back propagation neural network (BPNN) cannot fully learn data features, which leads to a decline in accuracy. The experimental results showed that the FCM algorithm can divide the marine sample data into corresponding categories to distinguish polluted and unpolluted samples. The improved neural network model has a higher degree of non-linear fit and lower prediction error than a back propagation (BP) neural network. The main contribution of this paper is to first study the spatial and temporal accumulation characteristics of marine environmental data. The academic contribution of this study is to substitute the predictions of the three gray models (GMs) with the neural network structure simulation to finally obtain more accurate predictions. From a practical point of view, this study is helpful to a certain extent in alleviating the pressure of climate change due to increased CEs in global coastal zones. This study can also provide a new method of measuring environmental governance for marine environmental regulatory authorities.

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“…With the growth of population and economy, cities have become home to more than half of the world's population, and countries and cities are inseparable. The study on CO 2 emissions of cities is currently a top trending topic, such as the impact of urban landscapes [7], transport [8,9], building [10][11][12] and energy consumption [13,14] on CO 2 emissions. In the course of China's urban development, single city development has gradually been replaced by urban clusters development.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Influencing Factors and Trend Forecast of CO2 Emission in Chengdu-Chongqing Urban Agglomeration

et al. 2022

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Urban agglomeration is a primary source of global energy consumption and CO2 emissions. It is employed as a major means of modern economic and social activities. Analysis of the temporal and spatial characteristics of CO2 emissions in urban agglomerations and prediction of the future trends of CO2 emissions in urban agglomerations will help in the implementation of CO2 reduction policies within region-wide areas. So, based on that, this study contains four aspects. Firstly, it calculates the energy CO2 emissions of China’s Chengdu-Chongqing urban agglomeration. Secondly, it analyzes the time and space changes in the area by using ArcGIS. Then, the STIRPAT model is used to investigate the factors influencing CO2 emissions, and the elasticity coefficient of the influencing factors is estimated using the ridge regression method, and the important influencing factors are screened on the basis of the estimated results, which are then used as input features for prediction. Finally, a combined prediction model based on the improved GM (1, N) and SVR models is constructed, and then the optimal solution is found through the particle swarm optimization algorithm. It sets up different CO2 emission scenarios to predict the energy CO2 emission of the region and its cities. The results show that, first, the CO2 emissions of the Chengdu-Chongqing urban agglomeration have accumulated year by year, but by 2030, as predicted, it will not reach its peak. The spatial layout of CO2 emissions in this region is not expected to undergo major changes by 2030. Second, population, GDP, gas and electricity consumption, and industrial structure have served as important factors affecting energy CO2 emissions in the region. Third, on the basis of the prediction results for different scenarios, the CO2 emissions in the baseline scenario are low in the short term, but the CO2 emissions in the low-carbon scenario are low in the long run. This study also puts forward some policy recommendations on how to reduce CO2 emissions.

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Forecast of urban traffic carbon emission and analysis of influencing factors

Cited by 35 publications

References 49 publications

Forecasting Chinese carbon emission intensity based on the interactive effect GM(1,N) power model

Forecasting Chinese carbon emission intensity based on the interactive effect GM(1,N) power model

Application of carbon emission prediction based on a combined neural algorithm in the control of coastal environmental pollution in China

Analysis of Influencing Factors and Trend Forecast of CO2 Emission in Chengdu-Chongqing Urban Agglomeration

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