环境约束下的投资不足与最优资本结构

IF 1.9 3区 经济学 Q2 ECONOMICS Journal of Economic Dynamics & Control Pub Date : 2023-10-16 DOI:10.1016/j.jedc.2023.104761
Pengfei Luo , Yingxian Tan , Jinqiang Yang , Yanming Yao
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引用次数: 0

摘要

我们为受环境约束(法规)的杠杆公司开发了一个q理论模型。该模型强调了环境约束对动态投资和最优资本结构决策的影响。我们发现,环境约束会导致股东产生内生的风险厌恶。此外,环境约束导致资本和资产销售投资不足,这归因于环境约束造成的碳减排效应。此外,相对于没有环境约束的企业,有环境约束的企业在低经营风险时采用保守负债,在高经营风险时选择高杠杆,这是由环境约束导致的消减成本效应和风险规避效应之间的权衡决定的。受环境约束的公司有较高的信用利差。我们的理论结果与一些实证结果是一致的。
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Underinvestment and optimal capital structure under environmental constraints

We develop a Q-theoretical model for levered firms subject to environmental constraints (regulations). The model highlights the implications of environmental constraints on dynamic investment and optimal capital structure decisions. We find that environmental constraints can lead equityholders to become endogenously risk averse. Moreover, environmental constraints give rise to underinvestment in capital and asset sales, which is attributed to the carbon abatement effect caused by environmental constraints. In addition, firms with environmental constraints use conservative debt for low business risk and choose high leverage for high business risk relative to firms without environmental constraints, which is governed by the trade-off between abatement cost effect and risk-aversion effect caused by environmental constraints. Firms with environmental constraints have high credit spreads. Our theoretical results are consistent with some empirical findings.

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来源期刊
CiteScore
3.10
自引率
10.50%
发文量
199
期刊介绍: The journal provides an outlet for publication of research concerning all theoretical and empirical aspects of economic dynamics and control as well as the development and use of computational methods in economics and finance. Contributions regarding computational methods may include, but are not restricted to, artificial intelligence, databases, decision support systems, genetic algorithms, modelling languages, neural networks, numerical algorithms for optimization, control and equilibria, parallel computing and qualitative reasoning.
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