Impact of Carbon Emission Trading Market on Regional Urbanization: an Empirical Study Based on a Difference-In-Differences Model

Cheng, Haoyuan; Liu, Xiaoqian; Xu, Zhenlong

doi:10.58567/eal01010003

Cited by 22 publications

(5 citation statements)

References 7 publications

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“…In addition, some other scholars have used CGE, logistic, and system dynamics theory models to predict the peak carbon emissions of different regions and industries, and their different scenarios have been analyzed (Meng and Niu 2011 ; Mirzaei and Bekri 2017 ; Beuuséjour et al 1995 ). Cheng et al ( 2022 ) evaluate that the carbon trading pilot policy can promote the upgrading of industrial structure in the region and ultimately achieve the reduction of industrial carbon emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The third industrial revolution has severely impinged on the Earth’s original ecology by bringing about massive consumption of fossil energy and triggering successive problems such as the greenhouse effect and global warming (Yang et al 2022 ; Chai et al 2022 ; Cheng et al 2022 ). IPCC reports revealing that the global average temperatures will be 1.5 to 4.8 degrees warmer than pre-industrial revolution levels by 2100 and that the impact of climate change on humankind’s socioeconomic situation is expected to worsen if existing climate trends are not controlled (Mondal et al 2022 ).…”

Section: Introductionmentioning

confidence: 99%

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When will China’s industrial carbon emissions peak? Evidence from machine learning

Ran

Razzaq

et al. 2023

Environ Sci Pollut Res

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The manufacture of products in the industrial sector is the principal source of carbon emissions. To slow the progression of global warming and advance low-carbon economic development, it is essential to develop methods for accurately predicting carbon emissions from industrial sources and imposing reasonable controls on those emissions. We select a support vector machine to predict industrial carbon emissions from 2021 to 2040 by comparing the predictive power of the BP (backpropagation) neural network and the support vector machine. To reduce noise in the input variables for BP neural network and support vector machine models, we use a random forest technique to filter the factors affecting industrial carbon emissions. The statistical results suggest that BP’s neural network is insufficiently adaptable to small sample sizes, has a relatively high error rate, and produces inconsistent predictions of industrial carbon emissions. The support vector machine produces excellent fitting results for tiny sample data, with projected values of industrial carbon dioxide emissions that are astonishingly close to the actual values. In 2030, carbon emissions from the industrial sector will have reached their maximum level.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

When will China’s industrial carbon emissions peak? Evidence from machine learning

Ran

Razzaq

et al. 2023

Environ Sci Pollut Res

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“…However, the current research is deficient in two aspects. Environmental issues have been a hot topic and scholars have been active in verifying the impact of different policies on it (Liu et al, 2022;Wu et al, 2021;Meng et al, 2022;Cheng et al, 2022). The number of studies that specifically focus on the relationship between urbanization and cultivated land is limited.…”

Section: Literature Reviewmentioning

confidence: 99%

Does urbanization reduce the multi-functional value of cultivated land? Evidence from China

Shi¹,

Liu

2023

Front. Environ. Sci.

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The contradiction between urbanization and cultivated land conservation needs to be resolved urgently. The coupling of cultivated land production, compound utilization and ecological protection is a hot topic for policymakers and researchers. Based on the panel data of 31 provinces in China from 2002 to 2018, this paper estimated the impact of urbanization on the quantity and structure of cultivated land functional value using a fixed effect model. The results show that the multi-functional value of cultivated land fluctuates in time series. There was obvious synergistic relationship among different functions of cultivated land, and the synergistic relationship was gradually weakened. Urbanization did not decrease the multi-functional value of cultivated land, and the producing function of cultivated land was improved most obviously. The value of producing function and social security function in main-producing areas is the most obvious response to urbanization. The culture of main marketing areas has the highest response to urbanization, and urbanization has reduced the ecological function of grain main marketing areas. Urbanization has reduced the diversified types of cultivated land, and the function positioning of cultivated land is gradually obvious. Therefore, policymakers should pay attention to the dialectical relationship between the total amount and structure of multi-functional cultivated land and implement regional differentiation policy of multi-functional cultivated land utilization.

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“…On the one hand, unlike traditional technological innovation, green innovation is characterized by “dual externalities” (Rennings, 1998). It is necessary for the government to take various incentive measures to guide the flow of innovation resources, but the intervention effect of green R&D is often characterized by uncertainties (Cheng et al, 2022; Zhao & Tian, 2022). While some environmental policies increase the cost of enterprises (Gray & Shadbegian, 2003; Greenstone et al, 2012), the innovation results are often contrary to expectations.…”

Section: Introductionmentioning

confidence: 99%

Sustainable development through green innovation and resource allocation in cities: Evidence from machine learning

Murai

Xie

et al. 2023

Sustainable Development

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China has promoted innovation-driven and green development to unprecedented strategic heights. However, compared to the large and rapid innovation investment, total factor productivity's (TFP) growth rate has shown a downward trend. Consequently, this study assesses the inefficiency caused by resource mismatch and discusses the impact of green innovation activities on green total factor productivity (GTFP). We use a causal forest-based machine learning method to solve the endogenous problem. The empirically analyzes the observation samples of 272 prefecturelevel cities in China from 2008 to 2018 and obtains the asymptotic normality estimation on the average treatment effect (ATE). Simultaneously, clustering causal forest and ridge expressions, discusses the robustness of related problems. According to the results, (1) the effect of China's green innovation on GTFP is negative for a short time and positive for a long time; (2) the impact of green innovation activities on GTFP is subject to capital mismatch, while the statistical law of the impact of labor mismatch is not obvious but the adverse impact of resource mismatch is gradually improving; and (3), Green innovation has significantly improved China's GTFP, but it did not lead to ideal Growth rate of GTC.

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Impact of Carbon Emission Trading Market on Regional Urbanization: an Empirical Study Based on a Difference-In-Differences Model

Cited by 22 publications

References 7 publications

When will China’s industrial carbon emissions peak? Evidence from machine learning

When will China’s industrial carbon emissions peak? Evidence from machine learning

Does urbanization reduce the multi-functional value of cultivated land? Evidence from China

Sustainable development through green innovation and resource allocation in cities: Evidence from machine learning

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