Trading, storage, or penalty? Uncovering firms' decision-making behavior in the Shanghai emissions trading scheme: Insights from agent-based modeling

排放交易 温室气体 方案(数学) 产业组织 业务 微观经济学 运筹学 环境经济学 经济 工程类 计算机科学 生态学 生物 数学分析 数学
作者
Yigang Wei,Xin Liang,Liang Xu,Gang Kou,Julien Chevallier
出处
期刊:Energy Economics [Elsevier]
卷期号:117: 106463-106463 被引量:33
标识
DOI:10.1016/j.eneco.2022.106463
摘要

The emissions trading scheme (ETS) has become a flagship climatic initiative for regulating greenhouse gas (GHG) emissions. Under an ETS, the emitting firm must simultaneously deal with changing carbon prices and the number of permits and the trade-off between permit trading (if one should buy, sell, or reserve) and permit-consuming production. This study characterizes the different compliance strategies' trade-offs, logic, and options among regulated emitting firms under an ETS. Consequently, this study provides firm-level evidence of regulated firms' strategic responses under the Shanghai ETS, considering detailed firm information, heterogeneous industry characteristics, and ETS architects. The proposed model simulated the trading interactions among diversified firms to portray the firms' decision-making process, and the emergent effects on the ETS market were identified. The results indicate that: 1) the carbon price experiences a non-monotonic “L-shaped” trend, which maintains an initial low level and increases sharply after crossing a threshold; 2) with increasing carbon prices, the trading in ETS becomes more active, especially among the low-emission firms; 3) the current ETS penalty is too limited and generic, which inadequately induces technological development and carbon reduction among firms. Finally, policy suggestions are provided for future optimization of the ETS mechanism. Overall, this study contributes to operation management literature by evaluating decision-making behaviors in a dynamic environment. Our findings have global implications for policymakers and managers in the private sector.
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
clover发布了新的文献求助10
刚刚
研友_VZG7GZ应助nihaoaaaa采纳,获得10
1秒前
1秒前
带虾的烧麦完成签到,获得积分10
1秒前
1秒前
hou发布了新的文献求助10
1秒前
2秒前
liucheng发布了新的文献求助10
3秒前
爱开心完成签到 ,获得积分10
3秒前
3秒前
ZKJ关闭了ZKJ文献求助
4秒前
suliang发布了新的文献求助10
4秒前
4秒前
想飞的猪发布了新的文献求助10
4秒前
问心发布了新的文献求助10
5秒前
梦行只为遇见你完成签到,获得积分10
5秒前
6秒前
6秒前
7秒前
7秒前
虚心谷梦发布了新的文献求助10
8秒前
高晗完成签到,获得积分10
8秒前
8秒前
18完成签到,获得积分10
8秒前
8秒前
Hello应助liucheng采纳,获得10
9秒前
缓慢黑猫发布了新的文献求助10
9秒前
10秒前
陈征发布了新的文献求助10
10秒前
daodao发布了新的文献求助10
11秒前
yorkson境发布了新的文献求助10
11秒前
moon发布了新的文献求助20
11秒前
微信发布了新的文献求助10
12秒前
华仔应助科研通管家采纳,获得10
12秒前
12秒前
田様应助icypz628采纳,获得10
12秒前
小斌发布了新的文献求助10
12秒前
NexusExplorer应助科研通管家采纳,获得10
12秒前
橘x应助科研通管家采纳,获得30
12秒前
12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
Handbook of pharmaceutical excipients, Ninth edition 5000
Aerospace Standards Index - 2026 ASIN2026 3000
Digital Twins of Advanced Materials Processing 2000
Polymorphism and polytypism in crystals 1000
Signals, Systems, and Signal Processing 610
Discrete-Time Signals and Systems 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 物理 生物化学 化学工程 计算机科学 复合材料 内科学 催化作用 光电子学 物理化学 电极 冶金 遗传学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 6041473
求助须知:如何正确求助?哪些是违规求助? 7782017
关于积分的说明 16234686
捐赠科研通 5187524
什么是DOI,文献DOI怎么找? 2775800
邀请新用户注册赠送积分活动 1758937
关于科研通互助平台的介绍 1642416