Innovation input-output and output-lagged input relationships of the next-generation information industry in China

Chen, Si; Huang, Wei‐Lun

doi:10.1016/j.ipm.2022.103066

Cited by 8 publications

(2 citation statements)

References 92 publications

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“…The study primarily relies on Asian Development Bank (ADB) data supplemented by the World Input-Output Database (WIOD), classifying the entire industry into 35 sectors. The study aligns inputoutput, energy consumption, and trade data classifications following adjustment methods proposed by Chen Si et al [21] and Liu Chang et al [22]. The study consolidates eight specific sectors into three major agricultural product industries (C1, C2, and C3) to address discrepancies between customs HS codes and input-output categories, enhancing clarity and consistency (see Table 1).…”

Section: Date and Sectoral Division Of Agricultural Productsmentioning

confidence: 99%

Study on Measurement and Influencing Factors of Trade Embodied Carbon-Based on China-Russia Agricultural Products Trade

Wei,

Li,

et al. 2024

Pol. J. Environ. Stud.

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China and Russia are major agricultural countries with abundant agricultural resources, holding important global agricultural trade market positions. While the scale of China-Russia agricultural trade has maintained rapid growth, the embodied carbon emissions from agricultural products have also increased. This article first uses input-output data to construct a multi-regional input-output model under a total trade accounting method to calculate the carbon emission coefficients, trade value-added, and embodied carbon emissions of three categories of agricultural products traded between China and Russia from 2009 to 2019. Our results show that, from an industry perspective, Category C3 agricultural products have the highest carbon emission coefficients; Category C1 agricultural products have the highest export value-added. From the standpoint of imports and exports, Russia's export value-added to China has proliferated in recent years and is slightly higher than that of Chinese exports to Russia; the embodied carbon emissions from Chinese agricultural products are higher than those from Chinese exports to Russia. We further examined the impact of trade scale, trade structure, and carbon emission intensity on embodied carbon, finding that these three factors have heterogeneous effects on the embodied carbon of agricultural trade for different industries and countries.

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Section: Date and Sectoral Division Of Agricultural Productsmentioning

confidence: 99%

Study on Measurement and Influencing Factors of Trade Embodied Carbon-Based on China-Russia Agricultural Products Trade

Wei,

Li,

et al. 2024

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

show abstract

“…In the related literature, the direction of the causality is somewhat ambiguous. There exists evidence indicating that the direction of causality is from R&D investment to firm profitability [57], while other studies have found insignificant causal relationships between the inputs and outputs of innovation in particular industries (e.g., [58]). Since the primary focuses in this paper are the estimation of the regional innovation value and the analysis of the spatial distribution of the regional innovation value in China, the theoretical relationship between the value of innovation outputs and inputs remains as presented in Equation ( 8), leaving the analysis of the causality for potential future studies.…”

Section: Extended Model: Regional Innovation Value Modelmentioning

confidence: 99%

The Sustainability of Regional Innovation in China: Insights from Regional Innovation Values and Their Spatial Distribution

Zhang

2023

Sustainability

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As the continuous improvement of the quality of innovation becomes increasingly significant for balanced regional development in China, it is critical to provide insights into the sustainability of regional innovation in China from the viewpoint of value. This study estimates regional innovation values based on an improved regional innovation value model incorporating patent values and a regional innovation indicator system. Data for invention patents as well as regional innovation indicators in 282 cities from 1987 to 2019 in China are utilized for estimation. Based on the estimated parameters and Monte Carlo simulation, city-level innovation values are calculated as benchmarks, along with provincial and regional innovation values, to analyze the patterns of the spatial distribution and agglomeration of regional innovation value. The findings are as follows. (1) The regional innovation value model provides an effective way to measure regional innovation in terms of value. (2) The regional innovation values are unevenly distributed; cities with higher innovation values are clustered in Eastern China, while most other cities have much lower innovation values. (3) The innovation values in Eastern China are notably higher, and the differences in innovation values between Eastern China and other regions are large and show a trend of first widening and then narrowing during the sample period. (4) The sustainability of regional innovation is not widely achieved, since highly concentrated innovation value is found in only a few regions in the eastern coastal areas. These findings suggest that promoting China’s innovation capacity and the sustainable development of technological innovation requires continually implementing innovation-driven development strategies, cultivating high-value innovation, optimizing industrial transfer, improving the layout of the national research infrastructure, giving full play to spatial spillover effects, and promoting interregional innovation information exchange in order to achieve the balanced and sustainable development of regional innovation.

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Driving toward business sustainability: Empirical evidence from low‐carbon practices

Wang,

Tuo,

Zhang

et al. 2024

Bus Strat Env

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This study investigated the effect of low‐carbon practices on business sustainability, delved into the underlying mechanisms that exist between them, and designed the recipes tailored toward promoting business sustainability in both high and low‐level. Based on a sample of 681 firms with 1376 observations from 2020 to 2022 that disclose the low‐carbon practices in MD&A, we employed a quasi‐replication method comprising regression analysis and fuzzy‐set qualitative comparative analysis (fsQCA) to test hypotheses and explore pathways. Regression analysis results demonstrate that low‐carbon practices significantly promote business sustainability and underscore the mediation effect of technological innovation capacity, alongside the moderating influence of R&D funding and government incentives and penalties. Additionally, fsQCA results reveal a pronounced synergistic impact emanating from multiple antecedent conditions, rather than a singular factor, in driving both high‐ and low‐level business sustainability. The findings on sustainable business operations management are invaluable for decision‐makers seeking to establish a low‐carbon strategic initiative that adeptly addresses the diverse opportunities and complexities posed by global environmental concerns.

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Innovation input-output and output-lagged input relationships of the next-generation information industry in China

Cited by 8 publications

References 92 publications

Study on Measurement and Influencing Factors of Trade Embodied Carbon-Based on China-Russia Agricultural Products Trade

Study on Measurement and Influencing Factors of Trade Embodied Carbon-Based on China-Russia Agricultural Products Trade

The Sustainability of Regional Innovation in China: Insights from Regional Innovation Values and Their Spatial Distribution

Driving toward business sustainability: Empirical evidence from low‐carbon practices

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