A Race Between Economic Growth and Carbon Emissions: How Will the CO2 Emission Reach the Peak in Transportation Industry?

Wu, Ya; Zhou, Yi; Liu, Yin; Liu, Jiawei

doi:10.3389/fenrg.2021.778757

Cited by 9 publications

(8 citation statements)

References 40 publications

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“…After confirming the occurrence of the long-term association by the ARDL test, we computed the parameters for the long-term associations. This result relates to the findings of [ 39 , 40 , 45 ].…”

Section: Resultssupporting

confidence: 90%

“…Torun et al [ 38 ] with the application of wavelet-based technique showed that CO 2 emissions and GDP growth have a positive interrupted short-term and strong unidirectional long-run relationship, thereby, specifying that economic growth propels environmental destruction. Wu et al [ 39 ] used the index of logarithmic mean divisia with the decoupling Tapio model to establish that CO 2 emissions peaks take into account air quality, path, and time in the transportation sector. They showed that the improved transportation sector fuelled CO 2 emissions to its peak.…”

Section: Introductionmentioning

confidence: 99%

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An autoregressive distributed lag approach for estimating the nexus between CO2 emissions and economic determinants in Pakistan

et al. 2023

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Carbon dioxide (CO2) emissions have become a critical aspect of the economic and sustainable development indicators of every country. In Pakistan, where there is a substantial increase in the population, industrialization, and demand for electricity production from different resources, the fear of an increase in CO2 emissions cannot be ignored. This study explores the link that betwixt CO2 emissions with different significant economic indicators in Pakistan from 1960 to 2018 using the autoregressive distributed lag (ARDL) modelling technique. We implemented the covariance proportion, coefficient of determination, the Durbin Watson D statistics, analysis of variance (ANOVA), variance inflating factor (VIF), the Breusch-Pagan test, the Theil’s inequality, the root mean quare error (RMSE), the mean absolute percentage error (MAPE), and the mean absolute error (MAE) for the diagnostics, efficiency, and validity of our model. Our results showed a significant association between increased CO2 emissions and increased electricity production from oil, gas, and other sources. An increase in electricity production from coal resources was seen to have resulted in a decrease in CO2 emissions. We observed that an increase in the gross domestic product (GDP) and population growth significantly contributed to the increased CO2 emissions. The increment in CO2 emissions resulting from industrial growth was not significant. The increment in CO2 emissions in the contemporary year is significantly associated with the preceding year’s increase. The rate of increase was very alarming, a sign that no serious efforts have been channelled in this regard to reduce this phenomenon. We call for policy dialogue to devise energy-saving and CO2 emission reduction strategies to minimize the impact of climate change on industrialization, population growth, and GDP growth without deterring economic and human growth. Electricity production from different sources with no or minimal CO2 emissions should be adopted. We also recommend rigorous tree planting nationwide to help reduce the concentration of CO2 in the atmosphere as well as environmental pollution.

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Section: Resultssupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

An autoregressive distributed lag approach for estimating the nexus between CO2 emissions and economic determinants in Pakistan

et al. 2023

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“…Gao and Sun [16] use the grey relational analysis model to investigate the evolutionary relationship of TCE and relevant factors in Jilin Province. Wu et al [17] combine the Logarithmic Mean Divisia Index (LMDI) model with the Tapio decoupling model, predict and analyze the time, path, and quality of carbon peaking in the transportation industry in China and its eastern, central, and western regions. Byers et al [18] have studied and analyzed the lowcarbon development measures of the UK transport system from 2010 to 2050 by building a practical energy analysis framework for future transport routes.…”

Section: Research On Transportation Carbon Emission Predictionmentioning

confidence: 99%

Research on Transportation Carbon Emission Peak Prediction and Judgment System in China

Sun,

Yang,

Liu

et al. 2023

Sustainability

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The transportation sector is a major contributor to carbon emissions, and managing its carbon peak is essential for China to reach the 2030 carbon peak target. This paper uses the autoregressive integrated moving average model (ARIMA) to design baseline scenarios and “double carbon” scenarios (carbon peak and carbon neutrality) based on the accounting of transportation carbon emissions in 30 provinces and cities in China to facilitate regional differentiation and forecast the development trend of transportation carbon emissions. Using the fuzzy comprehensive evaluation method, a comprehensive transportation carbon emission research and judgment system has been developed based on the forecast results. The research indicates a substantial increase in carbon dioxide (CO2) emissions from transport in China over the past 15 years, with an average growth rate of 5.9%, from 387.42 mt in 2005 to 917.00 mt in 2019. In the scenario prediction analysis, the overall carbon emission of the “two-carbon” scenario exhibits varying levels of reduction compared with the baseline scenario. According to the comprehensive research and judgment system, when the comprehensive evaluation index corresponding to the turning point year of transportation carbon emissions is greater than 0.85, and the index remains above 0.85 after the turning point, it can be judged that a region has achieved the peak of transportation carbon dioxide emissions under 95% possibility. It shows that China’s policies and strategies for carbon and emission reduction have played a significant role in transportation, but the low-carbon transformation and development still face great challenges.

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“…where i represents the province, t represents the year, and the dependent variable CI represents the carbon emission intensity, which is the explained variable; represents the carbon emission intensity, which is the explained variable; FD represents the independent variable financial development, which is the core explanatory variable; T represents technological progress; Z is the control variable, including regional openness (Open), human capital (Hc), infrastructure level (In f r), industrial structure (Stru), and urbanization level (Urb); Vi represents the individual effect, Vt represents the time effect,ε represents the random error term, and a 0 is the constant term of Model (3). The square term of financial development (FD 2 ) is added to the model to verify whether there is an inverted U-shaped relationship between financial development and carbon emissions.…”

Section: Empirical Modelmentioning

confidence: 99%

“…The Environmental Performance Index (EPI) assessment results released by global authorities in 2020 showed that China's EPI ranking remained low across the globe. Meanwhile, as extreme weather conditions increase, it is important to improve energy utilization, accelerate technological progress, reduce carbon emissions, and reach the carbon peak as soon as possible [2][3][4]. In China's Standards Plan, economic development, sci-tech progress, and ecological conservation are given equal importance.…”

Section: Introductionmentioning

confidence: 99%

Financial Development, Heterogeneous Technological Progress, and Carbon Emissions: An Empirical Analysis Based on Provincial Panel Data in China

Ren-zhong

Wei

2022

Sustainability

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Global warming, caused by an increase in carbon emissions, has attracted considerable attention worldwide. In addition, financial development affects technological progress and carbon emissions. Despite numerous works that explore the impact of financial development on technological progress and carbon emissions, few have integrated the three into a unified framework of research. To fill this gap, this study constructed a mediation effect model for empirical analysis based onthe provincial panel data of 30 provinces and cities in China from 2009 to 2021.Taking into account the regional differences across China, this study explored the impact of financial development on carbon emissions and the intermediary role that heterogeneous technological progress plays within. The results showed that at the national level, and in eastern and central China, the relationship between financial development and carbon emissions conformed to an inverted U, the environmental Kuznets curve, whereas in western China, carbon emissions were found to linearly increase with financial development. Among the variables of technological progress that served as mediators, generalized technology progress, environmental technology progress, energy technology progress, capital embodied technology progress, and FDI technology spillover were the transmission paths for the impact of financial development on carbon emissions at the national level. However, the effect of these variables of technological progress on the impact of financial development on carbon emissions varied among the different regions. This paper aims to provide some inspiration to reduce carbon emissions through financial development and prevent “one-size-fits-all” policies for technological advances or overall planning without considering regional differences.

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A Race Between Economic Growth and Carbon Emissions: How Will the CO2 Emission Reach the Peak in Transportation Industry?

Cited by 9 publications

References 40 publications

An autoregressive distributed lag approach for estimating the nexus between CO2 emissions and economic determinants in Pakistan

An autoregressive distributed lag approach for estimating the nexus between CO2 emissions and economic determinants in Pakistan

Research on Transportation Carbon Emission Peak Prediction and Judgment System in China

Financial Development, Heterogeneous Technological Progress, and Carbon Emissions: An Empirical Analysis Based on Provincial Panel Data in China

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