Greening China’s Growth: Assessing the Synergistic Impact of Financial Development and Technological Innovation on Environmental Pollution Reduction—A Spatial STIRPAT Analysis

Peng, Jiachao; Fu, Shuke; Gao, Dao Li; Tian, Jiali

doi:10.3390/ijerph20065120

Cited by 5 publications

(4 citation statements)

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“…The STIRPAT model, an important tool for environmental analysis, is derived from the IPAT environmental pressure equation proposed by Ehrlich et al and is widely used to analyze the non-proportional impact of human driving factors on the environment (Ehrlich and Holdren, 1971). In this paper, referring to the practices of Guo et al (2022b), Zhang et al (2023b), Peng et al (2023), andLiu et al (2023), a nonlinear STIRPAT model with multiple independent variables is constructed, and its basic formula is as follows:…”

Section: Analysis On Influencing Factors Of Agricultural Carbon Emiss...mentioning

confidence: 99%

Prediction of agricultural carbon emissions in China based on a GA-ELM model

Guo,

Yang,

Shen

et al. 2023

Front. Energy Res.

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Introduction: Strengthening the early warning of greenhouse gas emissions from agriculture is an important way to achieve Goal 13 of the Sustainable Development Goals. Agricultural carbon emissions are an important part of greenhouse gases, and accelerating the development of green and low-carbon agriculture is of great significance for China to achieve high-quality economic development and the goal of “carbon neutrality in peak carbon dioxide emissions”.Methods: By measuring the total agricultural carbon emissions in China and seven administrative regions from 2000 to 2021, the paper analyzes the influencing factors of agricultural carbon emissions in China by using STIRPAT environmental pressure model, and on this basis, predicts the peak trend of agricultural carbon emissions in China under different development scenarios by using the extreme learning machine model optimized by genetic algorithm.Results: The results showed that the extreme learning machine model improved by the genetic algorithm can overcome the shortcoming that the extreme learning machine model is easy to fall into the local optimal solution, thus obtaining higher prediction accuracy. At the same time, from 2000 to 2021, the total agricultural carbon emissions in China showed a continuous fluctuation trend, and due to the constraints of the agricultural economic level, agricultural industrial structure, and agricultural human capital, the agricultural carbon emissions showed spatial differentiation. It is worth noting that, in the context of green development, the agricultural carbon emissions of the seven regions in China all have the potential to achieve the “peak carbon dioxide emissions” goal in 2030, with only a slight difference at the peak.Discussion: The research results of this paper provide evidence for the government to formulate flexible, accurate, reasonable and appropriate agricultural carbon reduction policies, which is helpful to strengthen the exchanges and cooperation of regional agricultural and rural carbon reduction and fixation, and actively and steadily promote China's agriculture to achieve the goal of “peak carbon dioxide emissions carbon neutrality”.

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Section: Analysis On Influencing Factors Of Agricultural Carbon Emiss...mentioning

confidence: 99%

Prediction of agricultural carbon emissions in China based on a GA-ELM model

Guo,

Yang,

Shen

et al. 2023

Front. Energy Res.

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“…Furthermore, foreign direct investment (FDI) can crowd the market when introducing technology, thereby affecting the technological sophistication of the exports in the energy industry [10,13,33]. To measure FDI, we used the total amount of foreign registered investment in each province.…”

Section: Control Variablesmentioning

confidence: 99%

“…This increase in productivity reduces production costs and product prices, resulting in an increase in real income for the population and the related demand for consumer goods and services. Ultimately, this raises the labor demand for tasks that have not been replaced by related technological advances [6,10].…”

Section: Introductionmentioning

confidence: 99%

“…The application of industrial robots and the digitization in the manufacturing industry has also led to an improvement in the international division of labor status of service industries and the quality of service trade development [17]. Empirical studies have further demonstrated that industrial robots have different technological levels in the services industry [10,18,19].…”

Section: Introductionmentioning

confidence: 99%

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Disruptive Displacement: The Impacts of Industrial Robots on the Energy Industry’s International Division of Labor from a Technological Complexity View

Zhang

Peng

2023

Energies

Self Cite

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In light of the growing economic uncertainties worldwide, the use of industrial robots has emerged as a significant opportunity for improving the production efficiency and the international division of labor in China’s energy industry. This study employed a two-way fixed-effect model utilizing data from 31 Chinese provinces between 2011 and 2019 to investigate the impact of industrial robots on the energy industry’s participation in the international division of labor. The results of the study indicated that the widespread application of industrial robots can boost the international division of labor status of China’s energy sector. This conclusion remains robust even after addressing the potential endogeneity issues and conducting a range of sensitivity tests. Furthermore, our findings suggest that the regions that possess abundant energy resources or exhibit a lower carbon intensity are more likely to leverage the use of industrial robots to increase the technological sophistication and enhance their participation in the international division of labor. The application of industrial robots in the energy industry can enhance the international division of labor through two distinct channels: optimizing the factor structure and reducing the export costs. Our findings have important policy implications for ensuring energy security and improving the energy industry’s participation in the international division of labor.

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Significance of Geo-Visualization Tools for Pollution Monitoring

Mushtaq,

Farooq

2023

Geospatial Analytics for Environmental Pollution Modeling

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Greening China’s Growth: Assessing the Synergistic Impact of Financial Development and Technological Innovation on Environmental Pollution Reduction—A Spatial STIRPAT Analysis

Cited by 5 publications

References 50 publications

Prediction of agricultural carbon emissions in China based on a GA-ELM model

Prediction of agricultural carbon emissions in China based on a GA-ELM model

Disruptive Displacement: The Impacts of Industrial Robots on the Energy Industry’s International Division of Labor from a Technological Complexity View

Significance of Geo-Visualization Tools for Pollution Monitoring

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