亲爱的研友该休息了!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!身体可是革命的本钱,早点休息,好梦!

Evolutionary game theory analysis for understanding the decision-making mechanisms of governments and developers on green building incentives

激励 政府(语言学) 公共经济学 相互依存 有限理性 过程(计算) 情感(语言学) 跨国公司 微观经济学 业务 博弈论 产业组织 经济 营销 计算机科学 政治学 财务 哲学 法学 操作系统 语言学
作者
Ke Fan,Eddie C.M. Hui
出处
期刊:Building and Environment [Elsevier]
卷期号:179: 106972-106972 被引量:109
标识
DOI:10.1016/j.buildenv.2020.106972
摘要

Green building incentives are widely implemented. Under each incentive, governments and developers have different payoffs and dominant strategies that affect incentive effectiveness. Existing studies have examined incentive effectiveness through different methods but have failed to reveal the decision-making mechanisms of governments and developers in a dynamic process of a game. As governments and developers have bounded rationality, and their strategies may change from time to time, this study employed evolutionary game theory to model the evolutionary behaviours of two players, thus providing a quantitative method to illustrate the effectiveness of incentives and the strategy changes of the players. This study concluded that four types of interactions between governments and developers affect incentive effectiveness, namely, 1) governments' dominant strategies depend on developers' choices; 2) developers' dominant strategies rely on governments' choices; 3) two parties' dominant strategies are independent; 4) their dominant strategies are interdependent. Under these interactions, the price premium of green building and the level and affordability of incentives were found to be the critical factors for the decision makings of the leading players. Policy recommendations were proposed accordingly. This study adopted a mathematical approach to investigate the conflicts of interests between governments and developers. It also provided a general model which can fit various contexts. In addition, the research introduced a valuable angle of government payoffs. Results can advance policymakers' understanding of green building incentives, help policymakers predict developers' behaviours and the incentive effectiveness in the long run and justify the design or improvement of multinational incentives.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

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
水牛完成签到,获得积分10
1秒前
麻辣薯条完成签到,获得积分10
2秒前
时尚身影完成签到,获得积分10
5秒前
流苏完成签到,获得积分10
9秒前
流苏2完成签到,获得积分10
12秒前
12秒前
dqs发布了新的文献求助10
17秒前
早睡早起发布了新的文献求助10
20秒前
量子星尘发布了新的文献求助10
27秒前
早睡早起完成签到,获得积分10
29秒前
40秒前
呆萌剑封完成签到,获得积分20
41秒前
41秒前
赘婿应助dqs采纳,获得10
42秒前
Arthit完成签到 ,获得积分10
1分钟前
1分钟前
今后应助cactus采纳,获得10
1分钟前
OnlyHarbour发布了新的文献求助10
1分钟前
共享精神应助11采纳,获得10
1分钟前
呜呼完成签到,获得积分10
1分钟前
2分钟前
cactus发布了新的文献求助10
2分钟前
阳光以南完成签到,获得积分10
2分钟前
2分钟前
dqs发布了新的文献求助10
2分钟前
不一样的烟火完成签到 ,获得积分10
2分钟前
斯文败类应助薛雨佳采纳,获得10
2分钟前
桐桐应助dqs采纳,获得10
2分钟前
净净完成签到,获得积分20
2分钟前
晚星完成签到 ,获得积分10
2分钟前
科研通AI6应助sandaomi采纳,获得10
3分钟前
嘿嘿应助cactus采纳,获得10
4分钟前
4分钟前
4分钟前
思辰。发布了新的文献求助10
4分钟前
思辰。完成签到,获得积分10
4分钟前
shame完成签到 ,获得积分10
5分钟前
玉沐沐完成签到 ,获得积分10
5分钟前
科研通AI6应助哈哈哈采纳,获得10
5分钟前
5分钟前
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 Agriculture and Food Systems Third Edition 2000
Clinical Microbiology Procedures Handbook, Multi-Volume, 5th Edition 临床微生物学程序手册,多卷,第5版 2000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] | NHBS Field Guides & Natural History 1500
The Victim–Offender Overlap During the Global Pandemic: A Comparative Study Across Western and Non-Western Countries 1000
King Tyrant 720
T/CIET 1631—2025《构网型柔性直流输电技术应用指南》 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 5595721
求助须知:如何正确求助?哪些是违规求助? 4680968
关于积分的说明 14818191
捐赠科研通 4652213
什么是DOI,文献DOI怎么找? 2535586
邀请新用户注册赠送积分活动 1503530
关于科研通互助平台的介绍 1469764