Firm Relocation as Environmental Policy: Impacts on Agglomeration and the Environment

随着各国的工业化，促进经济增长与污染和排放等负面环境影响之间的权衡变得更具约束力。由于环境政策影响企业决策，它们为检验企业相互作用理论提供了一个视角，这些理论有助于我们理解企业行为。这个研究项目将研究将位于城市密集地区的公司迁往城市以外的工业地点的政策的影响。其目标是将污染企业迁出人口密度高的地区，这样它们就会影响更少的人。因为随着工业用地的出现，在新地点随机分配地块给公司，这种随机性使得较早搬迁的公司与较晚搬迁的公司具有可比性。这使得人们有可能更好地了解各种规模的工业活动如何影响城市地区的空气质量和排放，以及工人对居住地点的选择。此外，该提案还开发了一种从历史高分辨率卫星图像推断空气污染水平的新方法，这种方法在无法直接测量时很有帮助。这种创新的设计使研究人员能够评估产业政策对环境的影响。这项研究的结果将为环境政策提供投入，从而使美国成为环境研究和政策的全球领导者。了解环境政策的总体和分配后果对于制定平衡增长-污染权衡的政策至关重要。我们使用在不同时间将企业从城市中心随机移除的方法来评估邻里层面的政策对集聚、进入和退出、环境质量和土地价值等一系列结果的影响。分析的一个关键输入是跨越15年的精细分辨率空气质量测量。拟议的研究将使用应用于卫星图像的深度学习方法，以精细的空间和时间分辨率创建污染措施。此外，如果公司分配的地块属于四种大小类别之一，公司分配的具体地块也是随机的。这在公司的邻居中产生了随机变化，也在位置特征上产生了随机变化，比如与道路等基础设施的接近程度。该项目将评估企业区位的不同方面如何影响企业结果，以及这些影响如何根据邻居的特征而存在异质性。在团聚和密度的研究中，真正的实验变化几乎完全不存在，因此大多数识别要么是准实验的，要么是基于模型的。本研究首次对企业密度如何影响环境质量、企业成果和集聚进行了联合实验分析。这项研究的结果将为环境政策提供投入，从而使美国成为环境研究和政策的全球领导者。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

As countries industrialize, trade-offs between promoting economic growth and negative environment effects such as pollution and emissions become more binding. Because environmental policies impact firm decisions, they provide a lens to test theories of firm interactions that contribute to our understanding of firm behavior. This research project will study the effects a policy which relocated firms located in densely urban areas to industrial locations outside the city. The goal was to move polluting firms out of high population density areas so that they would affect fewer people. Because random drawings were held to assign plots at the new location to firms as industrial plots became available, the randomization makes the firms that relocated earlier comparable to those that relocated later. This makes it possible to provide an improved understanding of how the presence of industrial activity at a variety of scales affects air quality and emissions in urban areas, workers’ choices of where to live. Additionally, the proposal develops a new method of inferring air pollution levels from historical high-resolution satellite images which can be helpful whenever direct measurements are not available. This innovative design allows the researchers allows researchers to assess the impact of industrial policy on the environment. The results of this research will provide inputs into environmental policy and thus establish the US as the global leader in environment research and policy.Understanding aggregate and distributional consequences of environmental policies is important to inform policies that balance the growth-pollution trade-off. We use the randomized removal of firms from city centers at different times to assess neighborhood-level policy impacts on a range of outcomes such as agglomeration, entry and exit, environmental quality, and land values. A key input to the analysis is fine resolution air quality measures spanning a fifteen year period. The proposed research will use deep learning methods applied to satellite imagery to create pollution measures at a fine spatial and temporal resolution. Furthermore, conditional on a firm’s assigned plot being in one of four size categories, the specific plot that a firm allotted was also random. This generates random variation in firms’ neighbors, as well as in location characteristics such as proximity to infrastructure like roads. The project will estimate how different aspects of firm location impact firm outcomes and how these effects are heterogeneous depending on neighbors’ characteristics. In studies of agglomeration and density, truly experimental variation is almost entirely absent so that most identification is either quasi-experimental or model-based. This study is the first to conduct a joint experimental analysis of how firm density impacts environmental quality, firm outcomes, and agglomeration. The results of this research will provide inputs into environmental policy and thus establish the US as the global leader in environment research and policy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.