重庆理工大学学报2024，Vol.38Issue(8)：59-70,12.DOI:10.3969/j.issn.1674-8425(s).2024.04.005
汽车制造业绿色供应链管理对环境绩效的影响研究
The impact of green supply chain management on environmental performance in automotive manufacturing industry
摘要
Abstract
With the rapid development of our country's economy,the number of automobile owners,drivers,,automobile industries have continued to increase. It has promoted the progress of automobile manufacturing industry and also caused huge resource consumption and environmental pollution. Limited resources are no longer able to support the automobile manufacturing industry with heavily polluting,energy-consuming,low-utilization and dominated by an extensive economic growth model. In order to control carbon emission levels,the Chinese government proposed dual control targets for carbon emission reduction under the framework of the Paris Agreement,that is,by 2030,carbon emissions will reach a peak,and at the same time,carbon emission intensity will drop by 60％ to 65％ compared with that in 2005 . This requires the economic activities of manufacturing companies to pay more attention to environmental sustainability,and environmental performance has become an important indicator for the evaluation of green economic development. Therefore,manufacturing companies will face increasing institutional pressure and will gradually transform from traditional profit-centered production models to green,open,and symbiotic operating models. The implementation of green supply chain management by enterprises is an important strategy for improving the environmental performance of automobile manufacturing enterprises and promoting their sustainable development. At the same time,with the continuous emergence of emerging technologies such as the Internet,artificial intelligence,and electrification,the automobile industry has ushered in a new era. The new technological revolution will lead to changes in the structure of the entire automobile industry. New energy vehicles and intelligent network connectivity cars will gradually form a new development direction. Therefore,the ability of automobile manufacturing companies to analyze big data is not only the focus of their own transformation and upgrading,but also will open a new era for transformation and innovation,including innovative R&D,production,operations,marketing and management models. From"experience judgment"to"data portrait",artificial intelligence and other technologies to optimize automobile manufacturing processes and operational decisions are applied,as well as applied to procurement,material supply,quality control and supplier management to realize self-improvement and optimization of the supply chain. At the same time,the study integrates physical resources,supplier inventory,contract orders,production capacity,transportation capacity and big data of the entire industry chain to optimize the production system,maximize the value of material resources,further improve production efficiency,reduce production costs,and optimize the supply chain. The distribution system quickly responds to user's needs,promotes green supply chain management,and improves corporate environmental performance. Therefore,as our automobile manufacturing industry faces rapid industrial transformation and upgrading,how companies use big data analysis capabilities to improve environmental performance has become the key to promoting a new round of industrial revolution. In the context of green development and big data analysis promoting the transformation and upgrading of the traditional automobile manufacturing industry,this study explores the impact mechanism of institutional pressure on corporate environmental performance from the perspective of green supply chain management and big data analysis capabilities,and reveals the relationship between green supply chain management and big data analysis capabilities. Questionnaires were designed to investigate automobile manufacturing companies,275 pieces of valid data collected,and Smart PLS 2.0 used to establish a structural equation model (PLS-SEM) to analyze,process and verify the data. The main conclusions of this article are drawn:first,institutional pressure has a significant impact on the implementation of green supply chain management and big data analysis capabilities;secondly,green supply chain management and big data analysis capabilities have a significant impact on improving corporate environmental performance;thirdly,big data analysis capabilities have a significant impact on the implementation of corporate green supply chain management;fourthly,as for the impact of institutional pressure on environmental performance,there is a partial mediating effect between big data analysis capabilities and green supply chain management. By studying the interaction between institutional pressure,big data analysis capabilities,green supply chain management and environmental performance,the study can enrich the connotation of green supply chain management,clarify the important role of big data analysis capabilities,and expand the role of big data. The theory and application of supply chain innovation and operations management can also enrich the content and methods of the manufacturing environmental governance system. In practice,it provides new ideas and methods for automobile manufacturing companies to transform and upgrade,cultivate big data analysis capabilities,and improve environmental performance. It helps automobile manufacturing companies formulate effective big data analysis strategies and establish a new development model of conservation-oriented,low-carbon economy. It also helps to improve human well-being,promote social justice,reduce environmental damage,solve the problem of ecological scarcity,and promote the sustainable development of enterprises.关键词
汽车制造业/绿色供应链管理/环境绩效/制度压力/大数据分析Key words
automotive manufacturing industry/green supply chain management/environmental performance/institutional pressure/big data analysis分类
管理科学引用本文复制引用
代应,李晓佳,宋寒,于晓东..汽车制造业绿色供应链管理对环境绩效的影响研究[J].重庆理工大学学报,2024,38(8):59-70,12.基金项目
重庆市教育委员会人文社会科学重点项目"大数据驱动的制造业供应链协同减排路径及机制研究"(20SKGH160) （20SKGH160）
国家自然科学基金项目"面向大规模个性化需求的电商最后一公里协同配送研究"(71801025) （71801025）
