Regional uptake of direct reduction iron production using hydrogen under climate policy

生产(经济) 废品 软件部署 自然资源经济学 环境科学 气候政策 气候变化 业务 经济 工程类 生态学 机械工程 生物 软件工程 宏观经济学
作者
Steve Pye,Dan Welsby,Will McDowall,Tobias Reinauer,Olivier Dessens,Matthew Winning,Alvaro Calzadilla,Chris Bataille
出处
期刊:Energy and climate change [Elsevier BV]
卷期号:3: 100087-100087 被引量:21
标识
DOI:10.1016/j.egycc.2022.100087
摘要

The need to reduce CO2 emissions to zero by 2050 has meant an increasing focus on high emitting industrial sectors such as steel. However, significant uncertainties remain as to the rate of technology diffusion across steel production pathways in different regions, and how this might impact on climate ambition. Informed by empirical analysis of historical transitions, this paper presents modelling on the regional deployment of Direction Reduction Iron using hydrogen (DRI-H2). We find that DRI-H2 can play a leading role in the decarbonisation of the sector, leading to near-zero emissions by 2070. Regional spillovers from early to late adopting regions can speed up the rate of deployment of DRI-H2, leading to lower cumulative emissions and system costs. Without such effects, cumulative emissions are 13% higher than if spillovers are assumed and approximately 15% and 20% higher in China and India respectively. Given the estimates of DRI-H2 cost-effectiveness relative to other primary production technologies, we also find that costs increase in the absence of regional spillovers. However, other factors can also have impacts on deployment, emission reductions, and costs, including the composition of the early adopter group, material efficiency improvements and scrap recycling rates. For the sector to achieve decarbonisation, key regions will need to continue to invest in low carbon steel projects, recognising their broader global benefit, and look to develop and strengthen policy coordination on technologies such as DRI-H2.
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
玩家666发布了新的文献求助10
刚刚
Orange的应助被周七七采纳,获得10
1秒前
学术bug发布了新的文献求助10
1秒前
shanjiu完成签到,获得积分10
1秒前
JHIANG发布了新的文献求助10
1秒前
希望天下0贩的0的应助被pay采纳,获得10
2秒前
2秒前
情怀的应助被眼睛大唯雪采纳,获得10
3秒前
QQ完成签到,获得积分20
3秒前
研友_VZG7GZ的应助被调皮冷霜采纳,获得10
3秒前
3秒前
3秒前
pitte09完成签到,获得积分20
5秒前
5秒前
一念之间完成签到,获得积分10
5秒前
Feng发布了新的文献求助10
5秒前
6秒前
6秒前
tang发布了新的文献求助10
6秒前
傲娇的大门的应助被高光飞采纳,获得10
6秒前
6秒前
科研通AI6.2的应助被哦哟采纳,获得10
7秒前
8秒前
QQ发布了新的文献求助10
9秒前
11秒前
秒速五厘米的应助被七月流火采纳,获得10
12秒前
性感的面条完成签到,获得积分10
13秒前
14秒前
14秒前
陌上花开发布了新的文献求助10
15秒前
乌鲁鲁大师完成签到,获得积分10
16秒前
16秒前
今后的应助被兑为泽采纳,获得10
17秒前
苗条秋荷完成签到,获得积分10
17秒前
今后的应助被明智小聪采纳,获得30
17秒前
18秒前
18秒前
18秒前
You发布了新的文献求助10
19秒前
M二以发布了新的文献求助10
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
From Scaling and Perturbation to Stability 5000
Les Mantodea De Guyane 2000
Energie-Contracting 1500
Stochastic Models in Operations Research,Vol. 2 600
Problems and Solutions for Integer and Combinatorial Optimization: Building Skills in Discrete Optimization 600
Organizational Behavior 510
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 计算机科学 工程类 有机化学 化学工程 生物化学 物理 复合材料 内科学 催化作用 光电子学 人工智能 细胞生物学 无机化学 物理化学 数学
热门帖子
关注 科研通微信公众号,转发送积分 8030345
求助须知:如何正确求助?哪些是违规求助? 9469651
关于积分的说明 21239296
捐赠科研通 7600184
什么是DOI,文献DOI怎么找? 3388478
关于科研通互助平台的介绍 2520788
邀请新用户注册赠送积分活动 2425271