Toward carbon peak in China's manufacturing sector: Robot adoption and capital‐embodied technological progress

除数指数 温室气体 碳纤维 机器人 技术变革 环境经济学 产业组织 能源消耗 劳动力 自然资源经济学 制造业 情景分析 比例(比率) 工业机器人 业务 首都(建筑) 能量强度 索引(排版) 环境科学 高效能源利用 低碳经济 先进制造业 制造工程 资本成本 工程类 碳排放税 经济 计算机科学 碳捕获和储存(时间表) 涡轮机 工业生态学
作者
Xi Zhang,Xiaoqian Song,Mei-Ting Fan,Beijia Huang,Hongmei Yang,Shuai Shao,Yong Geng
出处
期刊:Journal of Industrial Ecology [Wiley]
标识
DOI:10.1111/jiec.70116
摘要

Abstract China's manufacturing sector has experienced increasing robot adoption and capital‐embodied technological progress, accompanied by massive energy consumption and carbon emissions. The robot adoption brings technological and environmental risks in the manufacturing sector. Based on the data of 28 manufacturing sub‐sectors, this study uses the logarithmic mean Divisia index method to investigate the contributions of robot adoption, labor, capital, and energy factors to the changes in carbon emissions in China's manufacturing sector. Furthermore, we conduct the scenario analysis and Monte Carlo simulation to project the future trajectories of carbon emissions in China's manufacturing sector under the different scenarios until 2035. Results show that during 2006–2019, both scale effect and technical effect driven by robots contributed to carbon emission reduction. Robot scale was the dominant contributor to the carbon emission increase, followed by capital automation. On the contrary, the workforce structure and energy‐robot structure played dominant roles in carbon emission reduction. Labor productivity, capital deepening, and the carbon intensity of energy exerted marginal effects on carbon emissions. During 2020–2035, carbon emissions will increase consistently from 62.4 million tons (Mt) to 72.6 and 228.2 Mt under the business‐as‐usual scenario and higher‐emission scenario, respectively, while they will have obvious inflection points under other three scenarios. Carbon emissions will peak at 65.3 Mt in 2023 and have the largest mitigation potential in the lower‐emission scenario. Finally, several policy suggestions are raised for China to build a manufacturing system with the coordinated development of intelligence and low carbon.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
2秒前
我是老大应助文献狗采纳,获得10
2秒前
Eunice发布了新的文献求助10
2秒前
3秒前
3秒前
无极微光应助sang采纳,获得20
4秒前
4秒前
azami完成签到,获得积分10
4秒前
5秒前
量子星尘发布了新的文献求助10
6秒前
崔懿龍发布了新的文献求助10
6秒前
科研喵发布了新的文献求助10
6秒前
痴情的雁易完成签到,获得积分10
7秒前
科研通AI6应助揽星采纳,获得10
8秒前
8秒前
8秒前
pb发布了新的文献求助10
10秒前
11秒前
李伟峰发布了新的文献求助10
11秒前
小兔子乖乖完成签到 ,获得积分10
12秒前
要开心吖完成签到,获得积分10
12秒前
12秒前
13秒前
14秒前
传奇3应助Patronus采纳,获得10
14秒前
15秒前
15秒前
front发布了新的文献求助10
16秒前
16秒前
科研通AI2S应助贝壳采纳,获得10
16秒前
文献狗发布了新的文献求助10
18秒前
19秒前
哇咔咔发布了新的文献求助10
19秒前
露露完成签到 ,获得积分10
19秒前
望远山完成签到,获得积分10
20秒前
Gauss应助朴素灯泡采纳,获得30
20秒前
隐形曼青应助zslf采纳,获得10
20秒前
Owen应助大力水手采纳,获得10
20秒前
20秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Encyclopedia of Reproduction Third Edition 3000
Comprehensive Methanol Science Production, Applications, and Emerging Technologies 2000
化妆品原料学 1000
1st Edition Sports Rehabilitation and Training Multidisciplinary Perspectives By Richard Moss, Adam Gledhill 600
小学科学课程与教学 500
Study and Interlaboratory Validation of Simultaneous LC-MS/MS Method for Food Allergens Using Model Processed Foods 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5643099
求助须知:如何正确求助?哪些是违规求助? 4760606
关于积分的说明 15020012
捐赠科研通 4801508
什么是DOI,文献DOI怎么找? 2566806
邀请新用户注册赠送积分活动 1524714
关于科研通互助平台的介绍 1484256