Agglomeration of the new energy industry and green innovation efficiency: Does the spatial mismatch of R&amp;D resources matter?

Li, Tuochen; Shi, Ziyi; Han, Dongri; Zeng, Jingwei

doi:10.1016/j.jclepro.2022.135453

Cited by 40 publications

(24 citation statements)

References 70 publications

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“…The Path C2 analysis: the industrial-structure effect of the GI The coefficients a 3 of the GI in columns ( 4) and ( 6) is significantly positive and negative, respectively, indicating that on the one side, the green innovation promotes the optimization of urban industrial structure; yet, on the other side, impedes the rationalization of industrial structure, which is not line with our expectation, but still consistent with some researchers findings that when faced with mandatory environmental policy or extreme "one-size-fits-all", the regulated plants have to reallocate original R&D resources and transfer them to green innovation, resulting in "crowding-out effect" of green innovation, even resource mismatch, and breaking the inherent coordination between different industries 77,78 . Other scholars have found empirically that there is threshold effect of environmental regulations on green transition of the industrial economy or green innovation 79 .…”

Section: Mechanism Discussion: the Mediating Role Of Green Innovationsupporting

confidence: 85%

The Ambient Air Quality Standards, green innovation, and urban air quality: Evidence from China

Zhang,

2023

Preprint

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As the Chinese economy entering the transition period, the environmental issues have become the one of most concerned topics. Based on panel data of 284 prefecture-level and above cities in China from 2006-2019, this study discusses the regulatory effects of the Ambient Air Quality Standards (AAQS) on urban air quality and its spatial spillover using the various DID models, and further builds a serial multiple mediation model to analyze the channel role of green innovation. The results reveal that (1) the implementation of the AAQS has a positive regulation effect on urban air quality, yet with a “inverted V-shaped” trend; (2) local implementation of the AAQS worsens the air quality of geographically adjacent cities within 400 km, while the effect is reversed for cities that are beyond 400 km; (3) heterogeneity analysis shows that the AAQS improves the air quality of central cities, medium-sized cities, cities of weak-intensity of environmental governance, recession or regeneration resource-based cities; and (4) it might reduce urban air quality only by inducing the green innovation per se. However, either by energy-use effect or industrial-structure effect triggered by the green innovation, can the AAQS favorably enhance urban air quality.

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Section: Mechanism Discussion: the Mediating Role Of Green Innovationsupporting

confidence: 85%

The Ambient Air Quality Standards, green innovation, and urban air quality: Evidence from China

Zhang,

2023

Preprint

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“…① Replacing the core explanatory variable: Based on Han et al (2023), this paper constructs a regional digital economy index from the digital environment, digital industrialization and industrial digitalization, to characterize the regional digitalization degree from the macro level [ 67 ]. We also use DIFF-GMM to re-estimate, as shown in column (1); ② Reducing the sample: According to the ranking of the mean value of the digital transformation, we removed 1 %, 5 % and 10 % of the sample, and re-estimated 28, 26 and 24 regions using DIFF-GMM [ 51 ]. Due to space constraints, this paper only reports the estimated results of 26 regional samples, see column (2).…”

Section: Empirical Test and Resultsmentioning

confidence: 99%

“…In addition, innovation performance measured by patent grant has been verified to have strong prior dependences [ 49 ]. To avoid the endogeneity problem, strong exogenous variable limitation and the early-stage dependency of patent grant, we refer to the previous studies [ 50 , 51 ] and combine the GMM estimation method with the threshold model to construct the dynamic threshold model, which introduced the lag terms of innovation performance

.…”

Section: Research and Designmentioning

confidence: 99%

Digital transformation, productive services agglomeration and innovation performance

Ding,

Shi,

et al. 2024

Heliyon

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“…Secara intuitif, peningkatan output yang dihasilkan dari R&D non-hijau dapat memperburuk kinerja lingkungan. Selain itu, mismatch antara pengeluaran R&D baik terhadap perbedaan harga aktual dan marginal output, spillover teknologi, dan distrupsi kelembagaan memengaruhi nilai efisien hijau secara negatif Li et al (2023). Dalam beberapa aspek di atas, pengeluaran R&D berpengaruh negatif terhadap nilai efisiensi hijau secara tidak langsung.…”

Section: Hasil Regresi Tobit Antara Efisiensi Hijau Terhadap Determin...unclassified

Evaluating Green Efficiency of Manufacture Sector and Its Determinants in East Java Province

Jamil

Putri

Suryaatmaja

2023

EJAVEC

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East Java is one of the provinces in Indonesia that carries out intensive development in the industrial sector to improve the welfare of its society. However, massive industrial activity will pose its challenges, such as increased emissions and environmental degradation. This study aims to determine the manufacturing sub-sector that produces CO2 emissions and has the lowest green efficiency as well as its determinant variables. There are three stages of analysis applied in this research i.e. carbon emission measurement, Slack Based Measurement-Data Envelopment Analysis (SBM-DEA), and Tobit regression. The study use 5-digit manufacturing subsector data based on the International Standard Industrial Classification (ISIC) system, obtained from the Indonesian Central Agency of Statistics (BPS) which is deflated against the East Java Regional Gross Domestic Product (GRDP) deflator with 2010 as the base year. The results of the analysis show that in general, the manufacturing subsector in East Java produces quite high CO2 emissions of 85.28 tons. The subsector with the highest average CO2 emissions in East Java in 2017 was the fertilizer industry, in 2018 the non-ferrous base metal manufacturing industry, and in 2019 the steel rolling industry. Then, the drying industry and tobacco depreciation became the sub-sector with the lowest average green efficiency in East Java Province in 2017. Meanwhile, in 2018 it was the milling industry of various nuts (including Leguminous) and in 2019 it was the coffee processing industry. The Tobit regression results show that the energy structure and industrial concentration have no significant effect on the green efficiency value of the manufacturing subsector. Meanwhile, spending on R&D has a significant negative effect on green efficiency. Therefore, the recommendations given are encourage the use of environmentally friendly raw materials and fuels, increasing green innovation, and increasing consumer awareness regarding environmental issues.

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Agglomeration of the new energy industry and green innovation efficiency: Does the spatial mismatch of R&D resources matter?

Cited by 40 publications

References 70 publications

The Ambient Air Quality Standards, green innovation, and urban air quality: Evidence from China

The Ambient Air Quality Standards, green innovation, and urban air quality: Evidence from China

Digital transformation, productive services agglomeration and innovation performance

Evaluating Green Efficiency of Manufacture Sector and Its Determinants in East Java Province

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