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加具有约束力。由于环境政策影响企业决策，它们提供了一个透镜来检验企业互动理论，有助于我们理解企业行为。本研究项目将研究将位于城市密集地区的企业搬迁到城市以外的工业地点的政策的影响。目标是将污染企业从人口密度高的地区转移出去，这样它们就不会影响到那么多人。由于随机抽签是为了在工业用地可用时将新地点的地块分配给公司，因此随机化使得较早搬迁的公司与较晚搬迁的公司具有可比性。这使得人们能够更好地了解各种规模的工业活动的存在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，在无法进行直接测量的情况下，这种方法可能会有所帮助。 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，从而建立美国作为全球领导者在环境研究和policy.Understanding环境政策的总体和分布后果是很重要的，为政策，平衡增长-污染权衡。我们使用在不同时间从城市中心随机删除的公司，以评估一系列的结果，如聚集，进入和退出，环境质量和土地价值的邻里层面的政策影响。分析的一个关键输入是跨越十五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，在公司分配的地块是在四个大小类别之一的条件下，公司分配的具体地块也是随机的。这会导致公司的邻居以及位置特征（如与道路等基础设施的接近程度）的随机变化。该项目将估计企业选址的不同方面如何影响企业的结果，以及这些影响如何根据邻居的特征而异。在对团聚和密度的研究中，几乎完全不存在真正的实验变化，因此大多数识别要么是准实验，要么是基于模型。这项研究是第一个进行联合实验分析企业密度如何影响环境质量，企业的成果，和集聚。这项研究的成果将为环境政策提供投入，从而确立美国在环境研究和政策方面的全球领导地位。该奖项反映了NSF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.