Keeping policy commitments: An organizational capability approach to local green housing equity
Keeping policy commitments: An organizational capability approach to local green housing equity
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信守政策承诺：地方绿色住房公平的组织能力方法
DOI：
10.1111/ropr.12499
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发表时间：
2022
影响因子：
2.1
通讯作者：
Stokan, Eric
中科院分区：
文献类型：
--
作者：
Deslatte, Aaron;Kim, Serena;Hawkins, Christopher V.;Stokan, Eric
将可持续发展目标纳入其设计的经济适用房有可能通过提高能源效率，结构耐久性和室内环境质量来解决健康和经济差异。尽管这些双赢的进步有潜力，但美国地方政府的调查数据显示，这类股权投资仍然很少见。本研究探讨了通过节能住房实现分配公平的障碍和途径。利用在美国联邦对地方政府能源效率计划的投资增加期间收集的存档城市可持续性调查数据，我们将联合收割机机器学习（ML）和过程跟踪方法结合起来，对这些决策背后的复杂驱动因素和障碍进行建模。首先，我们问，如何做一个城市的组织学习方法的特点，其行政结构，过去的经验与住房计划，资源，利益相关者的参与和规划预测政策承诺，绿色经济适用房？使用集成ML方法，我们发现，组织学习的三种特定模式-过去的经验与经济适用房计划，寻求帮助，从邻里团体和专业绿色组织的技术专长-是最有影响力的功能，在确定城市的承诺，建设绿色经济适用房。我们的第二阶段在ML模型识别的特定案例中使用过程跟踪，以确定这些因素的顺序，并为绿色住房政策的实施提供更多的细微差别。
Affordable housing that incorporates sustainability goals into its design has the potential to address both health and economic disparities via enhanced energy‐efficiency, structural durability and indoor environmental quality. Despite the potential for these win‐win advances, survey data of U.S. local governments indicate these types of equity investments remain rare. This study explores barriers and pathways to distributional equity via energy‐efficient housing. Using archival city sustainability survey data collected during a period of heightened U.S. federal investment in local government energy‐efficiency programs, we combine machine learning (ML) and process‐tracing approaches for modeling the complex drivers and barriers underlying these decisions. First, we ask, how do characteristics of a city's organizational learning methods—its administrative structure, past experience with housing programs, resources, stakeholder engagement and planning—predict policy commitments to green affordable housing? Using ensemble ML methods, we find that three specific modes of organizational learning—past experience with affordable housing programs, seeking assistance from neighborhood groups and the technical expertise of professional green organizations—are the most impactful features in determining city commitments to constructing green affordable housing. Our second stage uses process‐tracing within a specific case identified by the ML models to determine the ordering of these factors and to provide more nuance on green‐housing policy implementation.
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影响因子：
4.2
作者：
Manuel P. Teodoro
通讯作者：
Manuel P. Teodoro
我们对城市可持续发展了解多少？
影响因子：
4.7
作者：
William L. Swann;A. Deslatte
通讯作者：
A. Deslatte
影响因子：
2.4
作者：
C. Argyris
通讯作者：
C. Argyris
失败：扩散、学习和政策放弃
影响因子：
1.5
作者：
C. Volden
通讯作者：
C. Volden
完美的帮助量：城市能源和气候倡议中能力与合作之间关系的检验
影响因子：
2.1
作者：
Rachel M. Krause;Christopher V. Hawkins;Angela Y. S. Park
通讯作者：
Angela Y. S. Park