已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Pushing carbon footprint reduction along environment with carbon-reducing information asymmetry

信息不对称 碳足迹 比较静力学 私人信息检索 背景(考古学) 碳纤维 业务 政府(语言学) 利润(经济学) 产业组织 微观经济学 经济 环境经济学 计算机科学 温室气体 哲学 复合数 生物 古生物学 语言学 计算机安全 生态学 算法
作者
Jing Xia,Wenju Niu
出处
期刊:Journal of Cleaner Production [Elsevier BV]
卷期号:249: 119376-119376 被引量:27
标识
DOI:10.1016/j.jclepro.2019.119376
摘要

Pushing carbon footprint reduction (CFR) by means of carbon regulation has received increasing attention. However, extant research primarily focuses on the design of carbon regulation under complete information, neither the feature of carbon regulation under asymmetric information nor the role of private information in the government-firm relationship has been explored. In this paper, we investigate the issue of government-firm contracting for CFR in the context of carbon-reducing information asymmetry. Two cases are considered: The single asymmetric information case where the firm’s carbon-reducing effort is privately known to himself, and the dual asymmetric information case in which neither the firm’s carbon-reducing capacity nor his effort is visible to the government. Carbon contracting model in each case is developed by mechanism design and game theory. To examine the effectiveness of these carbon contracts, the benchmark with complete information is further studied. Comparative statics and sensitive analysis show that the carbon contracts under information asymmetry can efficiently motivate the firm to reduce carbon footprint. In particular, under single asymmetric information, the government appropriately adjusts the optimal contractual configurations to maximize the expected welfare, leaving the firm to get the reservation profit. Under dual asymmetric information, the government’s best choice is to offer a menu of carbon contracts which enables the firm to obtain extra information rent that is increasing in his carbon-reducing capacity. We explicitly identify conditions under which the screening mechanism works and reveal that the menu of carbon contracts not only induces the firm to reveal his true carbon-reducing capacity but also motivates him to make the best effort to reduce carbon footprint. Our findings provide the government with a theoretical basis regarding carbon regulation under information asymmetry, as well as help firms make appropriate selections when signing low-carbon contracts with the government.

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
4秒前
xxxxxxxx发布了新的文献求助10
8秒前
9秒前
10秒前
种下梧桐树完成签到 ,获得积分10
11秒前
平淡的康完成签到,获得积分10
11秒前
小球完成签到 ,获得积分10
13秒前
科目三应助汉堡9999号采纳,获得10
13秒前
khan发布了新的文献求助10
14秒前
负责秋烟完成签到 ,获得积分10
14秒前
oldcat完成签到,获得积分10
15秒前
15秒前
wukong完成签到,获得积分10
15秒前
16秒前
16秒前
16秒前
16秒前
所所应助科研通管家采纳,获得10
16秒前
16秒前
16秒前
静水流深完成签到,获得积分10
16秒前
soso发布了新的文献求助30
21秒前
量子星尘发布了新的文献求助10
21秒前
Jack完成签到,获得积分10
24秒前
科研通AI6.3应助Yyyyy采纳,获得10
28秒前
科研通AI6.2应助Yyyyy采纳,获得10
29秒前
十七岁男高中生完成签到,获得积分10
31秒前
SOESAN完成签到,获得积分10
33秒前
35秒前
877633629完成签到 ,获得积分10
35秒前
嘻嘻完成签到,获得积分10
36秒前
FISH完成签到,获得积分10
36秒前
虚幻的小海豚完成签到,获得积分10
37秒前
dart1023发布了新的文献求助10
37秒前
37秒前
38秒前
Chen完成签到 ,获得积分10
40秒前
Fearless发布了新的文献求助10
41秒前
FISH发布了新的文献求助10
41秒前
嘻嘻发布了新的文献求助10
41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
Kinesiophobia : a new view of chronic pain behavior 2000
Burger's Medicinal Chemistry, Drug Discovery and Development, Volumes 1 - 8, 8 Volume Set, 8th Edition 1800
Cronologia da história de Macau 1600
文献PREDICTION EQUATIONS FOR SHIPS' TURNING CIRCLES或期刊Transactions of the North East Coast Institution of Engineers and Shipbuilders第95卷 1000
BRITTLE FRACTURE IN WELDED SHIPS 1000
Lloyd's Register of Shipping's Approach to the Control of Incidents of Brittle Fracture in Ship Structures 1000
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 纳米技术 计算机科学 化学工程 生物化学 物理 复合材料 内科学 催化作用 物理化学 光电子学 细胞生物学 基因 电极 遗传学
热门帖子
关注 科研通微信公众号,转发送积分 6150298
求助须知:如何正确求助?哪些是违规求助? 7978972
关于积分的说明 16574827
捐赠科研通 5262503
什么是DOI,文献DOI怎么找? 2808625
邀请新用户注册赠送积分活动 1788845
关于科研通互助平台的介绍 1656916