Research on the Heterogeneity of Green Biased Technology Progress in Chinese Industries: Decomposition Index Analysis Based on the Slacks-based measure integrating

Meng, Yuxin; Liu, Lu; Xu, Zhenlong; Gong, Wei; Yan, Guanpeng

doi:10.58567/jea01020002

Cited by 35 publications

(4 citation statements)

References 58 publications

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“…Therefore, the Chinese government has firmly advocated that innovation is key strategic foundation for building a modern economic system (Tu et al 2023 ). Technological innovation, as the primary source of economic growth, plays a crucial role in enhancing local productivity, while also acting as the fundamental driving force in the overall progress of national economic development (Wu et al 2021 ; Fan et al 2022 ; Meng et al 2022 ).…”

Section: Introductionmentioning

confidence: 99%

The corporate path to green innovation: does the digital economy matter?

Wang

2023

Environ Sci Pollut Res

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As China’s digital transformation accelerates, there is growing interest in whether the digital economy can effectively boost green innovation in industrial enterprises and enable China’s development to break through the constraints of resources and environment. Therefore, this study analyzes the data of A-share industrial listed enterprises (2011–2020). Results indicate that the digital economy promotes green innovation. The impact of the digital economy on green innovation varies significantly among different types of enterprises, with stronger effects on state-owned enterprises. Digital economy enhances green innovation via boosting public attention and optimizing energy structure. Therefore, playing the role of monitoring public attention, and optimizing energy use are key strategies to promote corporate green innovation.

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Section: Introductionmentioning

confidence: 99%

The corporate path to green innovation: does the digital economy matter?

Wang

2023

Environ Sci Pollut Res

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“…Moreover, when economic development reaches a certain level, an environmentally sustainable economic development state will be formed due to the factors of low-carbon technology and environmental policies (Kaika and Zervas 2013 ). Meng et al ( 2022 ) factor bias in technological progress is determined based on data from 34 industries in China from 2000 to 2015. They found that China first tends to consume energy to pursue capital between capital input and energy input, while it tends to save energy after the Eleventh Five-Year Plan.…”

Section: Literature Reviewmentioning

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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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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“…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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Research on the Heterogeneity of Green Biased Technology Progress in Chinese Industries: Decomposition Index Analysis Based on the Slacks-based measure integrating

Cited by 35 publications

References 58 publications

The corporate path to green innovation: does the digital economy matter?

The corporate path to green innovation: does the digital economy matter?

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

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