Towards sustainable development in China: How do green technology innovation and resource misallocation affect carbon emission performance?

Du, Mingyue; Zhou, Quan; Zhang, Yunlai; Li, Feifei

doi:10.3389/fpsyg.2022.929125

Cited by 15 publications

(6 citation statements)

References 52 publications

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“…To substantiate the robustness of the regression results, referring to the study of Guo et al, 2020;Du et al, 2022b, this paper replaces the core explanatory variable digitalization with the Peking University Digital Financial Inclusion Index (DIGF) for regression analysis.…”

Section: Core Explanatory Variablementioning

confidence: 99%

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Digitalization, resource misallocation and low-carbon agricultural production: evidence from China

Wang

et al. 2023

Front. Environ. Sci.

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With the rapid development of digital technologies such as artificial intelligence, big data and cloud computing, China’s agricultural production is entering a new era characterized by digitalization. Based on provincial panel data of China from 2013 to 2020, this paper adopts the system GMM and mediating effects model to systematically examine the impact of digitalization on low-carbon agricultural production from the perspective of resource misallocation. The results indicate that digitalization can significantly curb agricultural carbon emissions and thus promote low-carbon agricultural production, and this finding still holds after the robustness test. The heterogeneity analysis indicates that the inhibiting effect of digitalization on agricultural carbon emissions is most pronounced in the eastern region relative to the central and western regions (the regression coefficients are −0.400 and −0.126 respectively). Further mechanism analysis suggests that digitalization can reduce agricultural carbon emissions by correcting the widespread capital and labor misallocation in agricultural factor markets. The findings of this study provide significant policy implications for low-carbon agricultural production in China.

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Section: Core Explanatory Variablementioning

confidence: 99%

“…A i is the productivity of the farmer. α ki and α li are the output elasticity of capital and labor, calculated by the Solow residual method (Du et al, 2022a). Meanwhile, there exists α ki + α li 1 under the assumption of CRS.…”

Section: Mediating Variablementioning

confidence: 99%

Digitalization, resource misallocation and low-carbon agricultural production: evidence from China

Wang

et al. 2023

Front. Environ. Sci.

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“…The level of economic development can influence environmental resource demand and energy utilization efficiency, thus affecting environmental pollution. This study adopts the per capita GDP of each province to measure economic development (Du et al, 2022b).…”

Section: Control Variablesmentioning

confidence: 99%

Spatial and nonlinear effects of local government debt on environmental pollution: Evidence from China

Huang²,

Du³

2022

Front. Environ. Sci.

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As a way for the government to integrate funds and make up for fiscal deficits, bond issuance plays an important role in environmental governance and thus impacts environmental quality. Based on the panel data of 30 provinces in China from 2011 to 2019, this study utilizes spatial econometric and threshold panel models to investigate the influence of local government debt on environmental pollution. The empirical results are as follows: 1) Both environmental pollution and local government debt have a significant positive spatial agglomeration effect. 2) The relationship between local government debt and environmental pollution presents a nonlinear U-shaped curve. Specifically, when the debt scale is less than the threshold value, local government debt plays a role in restraining environmental pollution, and when the debt scale exceeds the threshold value, the influence of local government debt on environmental pollution plays a promoting role. 3) A regional heterogeneity test suggests that in the eastern region, the influence of local government debt on environmental pollution is always restraining, while in the central and western regions, this influence is consistent with the national level, showing a U-shaped feature. This study provides a reference for policy-makers in improving environmental quality from the perspective of local government debt. Policy-makers should keep the scale of government debt within a moderate range to avoid the deterioration of environmental quality caused by excessive debt.

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“…In particular, the fashion industry is the second largest source of pollution after the petrochemical industry (Ostermann et al, 2021), so contemporary fashion companies must adopt a green technology innovation strategy to alleviate the environmental impacts of products. Indeed, green technology innovation strategy has been confirmed as a key source of sustainability (Du et al, 2022). Therefore, it is urgent to explore how to drive fashion companies to adopt green technology innovation strategy.…”

Section: Introductionmentioning

confidence: 99%

Why fashion companies adopt green technology innovation strategy: A perspective of environmental social responsibility

Wang

2023

Front. Environ. Sci.

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Towards sustainable development in China: How do green technology innovation and resource misallocation affect carbon emission performance?

Cited by 15 publications

References 52 publications

Digitalization, resource misallocation and low-carbon agricultural production: evidence from China

Digitalization, resource misallocation and low-carbon agricultural production: evidence from China

Spatial and nonlinear effects of local government debt on environmental pollution: Evidence from China

Why fashion companies adopt green technology innovation strategy: A perspective of environmental social responsibility

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