Carbon neutrality and green technology innovation efficiency in Chinese textile industry

Xiao-feng, XU; Cui, Xiaodan; Zhang, Yifan; Chen, Xiangyu; Li, Wenhong

doi:10.1016/j.jclepro.2023.136453

Cited by 29 publications

(13 citation statements)

References 87 publications

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“…At present, studies on green innovation efficiency are primarily conducted at the regional level [11][12][13] and industry level [14][15][16][17][18][19]. However, at the enterprise level, there is a relative lack of research on green innovation efficiency [20][21][22]. The input-output variables of green innovation efficiency are summarized in Table 1.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Slack Based Measure Model for Evaluating Green Innovation Efficiency Based on Asymmetric Data

Chen,

Xie,

Tao

2024

Symmetry

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Nowadays, one of the main challenges facing green innovation management is how to enhance the performance of innovation processes by utilizing asymmetric input and output data. Therefore, this paper develops an improved SBM model analysis framework for evaluating the green innovation efficiency of asymmetric input and output data. The framework is applied to assess the technical (TE), managerial (PTE), and scale (SE) efficiencies of new energy companies under three input variables (R&D personnel input, R&D capital input, and comprehensive energy consumption input), two desirable output variables (green technology output and economic output), and one undesirable output variable (greenhouse gas emissions). Then, environmental factors and random factors are eliminated from the obtained input slack variables based on the SFA model, placing decision-making units in a homogeneous environment. The results demonstrate that TE, PTE, and SE are improved after eliminating environmental factors and random factors. Subsequently, based on the entropy method, this paper classifies companies’ green innovation patterns into four categories and provides targeted solutions. The purpose of this paper is to provide an evaluation method for new energy companies to understand green innovation efficiency and assist decision makers in identifying the most optimal resource allocation approach. The proposed improved SBM model contributes to the literature and to industry practice by (1) providing a reliable evaluation of green innovation efficiency under asymmetric input and output data; (2) determining effective improvement actions based on a slack analysis of environmental variables and random variables that lead to improved process performance; and (3) making fuzzy innovation performance efficient to facilitate understanding and managing innovation resource allocation quality.

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Section: Literature Reviewmentioning

confidence: 99%

An Improved Slack Based Measure Model for Evaluating Green Innovation Efficiency Based on Asymmetric Data

Chen,

Xie,

Tao

2024

Symmetry

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“…This study quantified the volume of patent filings for green technology innovations, drawing upon the findings of Xu Jia and colleagues [32] Table 6. The results related to the green technology innovation mechanism are detailed in columns ( 7) through (9) of Table 6.…”

Section: Green Technology Innovation(gti)mentioning

confidence: 99%

Digital Transformation and High-Quality Development of Enterprises: Evidence from China

Hong

2024

TEBMR

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The swift progression of digital technology is leading to substantial shifts and evolutions within enterprises. In the evolving environment characterized by the integration of digital technologies and the physical market, it's essential to examine the effects of digitalization on the path of enterprises towards attaining superior growth quality. This study utilizes panel data from A-share listed companies in China's equipment manufacturing sector, spanning from 2013 to 2021, to perform an empirical analysis on the influence and mechanisms of digital transformation in fostering the progress of these companies towards high-quality growth. The results reveal that digital transformation plays a significant role in promoting high-quality development among equipment manufacturing enterprises, and these results remain consistent after implementing various tests to mitigate endogeneity and confirm the findings' reliability. The examination of mechanisms suggests that digital transformation significantly contributes to the advancement of equipment manufacturing companies by alleviating financial constraints, improving internal control quality, and facilitating the integration of environmentally friendly innovation technologies. Moreover, the impact of digital transformation on elevating the quality of development within equipment manufacturing companies is notably more pronounced in privately-owned firms, those located in the eastern region, and businesses classified as high-tech. This highlights how the nature of ownership, geographical location, and technological classification further amplify the benefits of digital transformation for high-quality growth in the sector. These findings offer a theoretical basis for assessing the economic implications of enterprises' digital transformation and for shaping relevant governmental policies.

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“…Cutting‐edge data analytics and tools for making decisions have become essential elements of innovation in green technology. The body of research highlights how data‐driven methods may be used to find ways to save energy, streamline operations, and encourage environmentally friendly decision‐making (J. Wang, Liu, et al, 2023; Xu et al, 2023). Businesses may minimize carbon emissions by identifying inefficiencies, gaining insights into their energy consumption patterns, and implementing targeted interventions by utilizing big data, machine learning, and AI (Lee et al, 2022; Q. Luo et al, 2019).…”

Section: Literature Review and Hypothesis Developmentmentioning

confidence: 99%

Artificial intelligence (AI)‐driven strategic business model innovations in small‐ and medium‐sized enterprises. Insights on technological and strategic enablers for carbon neutral businesses

Shaik,

Alshibani,

Jain

et al. 2023

Bus Strat Env

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This study investigates the enhancement of technological and strategic enablers for carbon‐neutral businesses (CNB) through artificial intelligence (AI)‐driven business model innovation (AIDBMI). Drawing upon the insights gained from the literature review, the study employs structural equation modeling (partial least squares structural equation modeling [PLS‐SEM]) as the methodology to examine the relationships between AIDBMI, technological enablers, strategic enablers, and the attainment of carbon neutrality. The sample size consists of 326 small‐ and medium‐sized enterprises (SMEs) in the United States of America. The findings of this study affirm the significant positive relationships between AIDBMI and both technological and strategic enablers for CNB. The utilization of AI technologies proves to be instrumental in fostering the development and implementation of innovative business models that integrate sustainability practices and address environmental challenges. By leveraging AIDBMI, SMEs can harness technological advancements to adopt energy‐efficient processes, embrace renewable energy solutions, and implement effective carbon reduction strategies. Moreover, the study highlights the critical role of strategic enablers in driving the transition towards carbon neutrality. The alignment of sustainability goals with organizational strategies, stakeholder collaboration, and employee engagement emerge as pivotal factors in enabling SMEs to effectively utilize AIDBMI and leverage technological advancements to achieve carbon neutrality. The implications of this study contribute to the existing literature by highlighting the importance of technological and strategic enablers in creating CNB. By integrating AIDBMI, organizations can drive sustainable transformations, optimize their operations, and align their resource management with sustainable practices. These insights provide valuable guidance for SMEs, policymakers, and researchers seeking to foster sustainable practices, promote carbon neutrality, and contribute to the advancement of a low‐carbon economy.

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Carbon neutrality and green technology innovation efficiency in Chinese textile industry

Cited by 29 publications

References 87 publications

An Improved Slack Based Measure Model for Evaluating Green Innovation Efficiency Based on Asymmetric Data

An Improved Slack Based Measure Model for Evaluating Green Innovation Efficiency Based on Asymmetric Data

Digital Transformation and High-Quality Development of Enterprises: Evidence from China

Artificial intelligence (AI)‐driven strategic business model innovations in small‐ and medium‐sized enterprises. Insights on technological and strategic enablers for carbon neutral businesses

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