Environmental Justice in Warehousing Location

Abstract

With the rapid spatial expansion of the warehousing industry in major metropolitan areas, environmental impacts associated with warehousing activities have been growing in the recent decades. This study focuses on the disproportionate distribution of warehousing facilities in disadvantaged neighborhoods and discusses how the disparities result from the interactions between various socioeconomic processes. From the perspective of environmental justice, warehousing-related environmental hazards affect the spatial relationship between warehouses and local communities. The changing factors in the firm location choice of warehousing facilities and the housing location choice of disadvantaged population jointly lead to the environmental justice problem in warehousing location.

Keywords

environmental justice warehousing location spatial distribution disadvantaged population

The last decades have witnessed substantial growth in the demand for goods movement between countries and cities, driven by booming global trade and economic cooperation. For instance, the volume of international merchandise exports has almost quadrupled in two decades. Merchandise trade accounted for 49 percent of total gross domestic product in the world in 2014, while the share in 1994 was only 31 percent (World Bank 2015a). The steady flow of commodities across boundaries strongly supports economic growth and also shapes the way regions and cities are organized.

An outstanding characteristic of goods movement in the era of globalization is its highly concentrated nature. A few major countries and gateway cities generate and attract the vast majority of trade volumes. In 2014, the top ten economies export almost half of the total merchandise in the world (World Bank 2015b). In the state of California, the United States, the share of commodity flows in terms of dollar values originating from four major metropolitan areas, Los Angeles, San Francisco, San Diego, and Sacramento, reached 90 percent in 2012 (Freight Analysis Framework 2012). Moreover, the high concentration of commodity flows greatly affects how the warehousing facilities are distributed across metropolitan areas. For instance, statistics showed that the aforementioned four metro areas in California also dominated the warehousing and storage industry,¹ and all the other metro areas and rural areas contain only 13 percent of warehousing establishments in the state (US Census Bureau 2016). Such dominance is subject to the characteristics of each metro area, including the roles in global trade, industrial policies, and geographic constraints (Giuliano, Kang, and Yuan 2016a).

The spatial expansion of the warehousing industry within metropolitan areas, which has reached an unprecedented level in the last two decades, is affected by various changes in supply chain management, logistics services, and urban structure. Large tracts of land in the suburbs have been developed as warehousing uses, and these spacious facilities have dramatically altered local built environment and landscape. One of the primary drivers that lead to the rapid expansion of the warehousing industry is the ongoing supply chain restructuring. As customers (such as retailers in the city centers) tend to reduce inventory space to save land rents, warehousing firms not only enhance their capabilities in storage and distribution of goods but also become more specialized in inventory management, product processing, packaging, and other activities (Bowen 2008; Cidell 2011; Rodrigue 2008). Moreover, the firms make efforts to improve transport efficiency in response to customers’ rising demand for more frequent and timely deliveries. Enhanced infrastructure and advanced technologies of transport and warehousing, on the other hand, provide essential support for these changes. Transport access to freeways, airports, and intermodal terminals becomes increasingly available throughout large metropolitan areas. Containerization, multimodalization, and just-in-time traffic systems make more efficient and responsive logistics services possible (Cidell 2011).

Another evolutionary change in warehousing industry is that warehouses and distribution centers (W&Ds) reduce labor costs by introducing highly automated systems. To accommodate the sizable automated systems and achieve economies of scale, developers build large structures that consume much land space (Andreoli, Goodchild, and Vitasek 2010). Finally, land prices go up, reshaping the urban form, and such changes also strongly influence how warehouses are spatially distributed. Compared to many other industries, the warehousing industry produces low per space unit revenues and thus has limited advantages in competition over high-value land in dense urban areas. As the urban economic theory predicts, the warehousing industry would bid less than industries like financial and professional services and move away from urban cores in response to the appreciation of land values (Giuliano et al. 2016b). Considering the aforementioned changes in the warehousing industry, cheap land available in the suburbs becomes increasingly popular among developers.

Logistics services are an essential part of the global supply chain, which enables the efficient functioning of the economy; however, the subsequent environmental impacts are becoming a growing concern to the public and policy makers. Warehousing uses consume much land, greatly alter the built environment, generate air pollution and noise, and contribute to severe pavement damage. These environmental impacts have been growing in terms of both geographic scale and magnitude. Local residents become more aware of the potential environmental threats from unregulated warehousing activities and regard warehouses as one of the locally undesirable land uses (LULUs). Such impression has increasingly affected the spatial relationship between W&Ds and adjacent communities.

The problem of environmental justice in warehousing location is defined as the disproportionate distribution of warehousing facilities in neighborhoods with high percentages of the socially disadvantaged population. If we categorize all neighborhoods based on median household income and percentage of minority population,² we can find in Figures 1 and 2 below that such environmental disparities do exist³ in the Los Angeles region and San Francisco region, although these two regions are different in various aspects ranging from land availability to industrial strategies. To empirically test the existence of environmental justice problem in warehousing location is beyond the scope of this article; however, the maps offer adequate incentives to first theoretically illustrate the concept and its mechanisms.

Figure 1.

Spatial distribution of warehouses and selected types of neighborhoods in the Los Angeles region.

Figure 2.

Spatial distribution of warehouses and selected types of neighborhoods in the San Francisco region.

This article aims to evaluate the location of warehouses from the perspective of environmental justice and understand the disproportionate distribution pattern of warehouses in disadvantaged neighborhoods by reviewing the relevant literature. Given the limited existing work on this topic, this article would first summarize the environmental externalities of warehousing activities and then discuss recent research progress on environmental justice. Finally, by synthesizing findings on the firm location choice of warehousing facilities and the housing location choice of disadvantaged population, this article develops a framework for theoretically understanding the socioeconomic dynamics. This article therefore includes the following sections: (1) warehouses, externalities, and environmental justice; (2) progress of research on environmental justice; and (3) location choices of warehousing facilities and disadvantaged population.

Warehouses, Externalities, and Environmental Justice

The existing literature does not directly link warehouses to environmental justice yet, but such a connection is getting increasingly necessary, given that local residents become more and more aware of the warehousing-related externalities (Newman 2012; Esquivel 2015). The environmental impacts of warehousing facilities and activities have been growing as the logistics demand and freight vehicle kilometers traveled increase (Browne et al. 2012). First, the substantial expansion of warehousing land uses greatly affects the urban landscape and the built environment of local neighborhoods. The newly built warehousing buildings are normally no more than two stories, but their width could be as long as 2,400 feet. The addition of these big boxes to the host neighborhoods probably contributes to more severe urban heat island effects (Aniello et al. 1995; Voogt 2007) and threats of stormwater runoff (Yang and Li 2013). Considering the minimum parking requirements, warehousing projects usually end up with huge land coverage and very low densities. Similar to surface parking lots (Lowe 1990; Ben-Joseph 2012), warehousing facilities increase the dimension of street blocks and further reduce the livability of nearby communities. For instance, the World Logistics Center in the City of Moreno Valley in California is planned to occupy 1,056 acres and the floor space of the entire warehousing cluster totals around forty million square feet (City of Moreno Valley 2016). Such a project may dramatically alter the appearance of the neighborhood and further affect the way local residents interact with their living environment. Second, the goods stored in W&Ds may be environmentally threatening, especially when they are inflammable, explosive, or toxic.

The third and the most important one is the environmental impacts of logistics activities affiliated with W&Ds, most of which occur outside of the buildings. Freight trucks regularly enter and exit warehouses. The movement, operation, and maintenance of trucks create considerable externalities. A study (Dablanc 2013) showed that trucks generate 59 percent of the PM₁₀, 43 percent of the SO₂, and 38 percent of the NO_x in total transport-related emissions in the Ile-de-France (the Parisian Region). According to the 2014 National Emissions Inventory data, heavy-duty vehicles account for 21 percent of the PM₁₀ and 51 percent of the NO_x among all on-road emission sources in the Los Angeles combined statistical area (US Environmental Protection Agency [EPA] 2014; nationwide statistics can be found in Liu et al. 2015). The contribution of freight vehicles to air pollution is thus significant relative to their share in the entire vehicle population. In spite of recent initiatives to promote cleaner trucks, freight vehicles are still a primary environmental polluter in the transportation sector. Various studies have documented the exposure of local residents to truck-related emissions and corresponding health outcomes including asthma and respiratory allergy in areas with high densities of truck activities (e.g., Lena et al. 2002; Kim et al. 2004; Kozawa, Fruin, and Winer 2009). Apart from air pollution, trucks also generate a high level of noise and disproportionately contribute to pavement damage (Dong et al. 2014; Cidell 2015). A study on the city of San Francisco finds that a heavy truck generates noise equivalent to over twenty-two automobiles (Seto et al. 2007). Taking the World Logistics Center as an example, this project would generate over 68,000 vehicle trips per day, 14,000 of which would be trucks (LA Times Editorial Board 2015). Given the potential environmental externalities, the project has been strongly criticized by local residents and environmental activists (e.g., Ghori 2015; Esquivel 2015). After all, externalities related to warehousing operation potentially impair local environment, property values, and quality of life.

Given these environmental externalities, W&Ds are probably regarded as locally undesirable. However, they are different from the traditional research subjects in the environmental justice literature, LULUs such as toxic facilities and landfills in several ways. First, the majority of the externalities are from truck movement around warehouses instead of activities within the facilities. As trucks move from different directions and at different times, warehousing-related impacts can extend to any adjacent neighborhoods with truck footprints (Aljohani and Thompson 2016). In spite that designated truck routes and truck operation time control strategies can possibly help mitigate truck-related impacts on local communities (e.g., Anderson, Allen, and Browne 2005; Allen, Thorne, and Browne 2007; Holguín-Veras et al. 2011), these impacts remain severe in many neighborhoods with intensive warehousing activities nearby (Seto et al. 2007; Ross et al. 2011). Second, W&Ds are much more widely spread than most traditional LULUs. The rapid growth of warehousing demand and development may further lead to a significant increase in the environmental burdens on local communities. Third, while some LULUs such as landfills are primarily at sites with very few residents nearby, warehousing facilities can be found in relatively dense areas where the exposure of local residents to warehousing-related pollution is a major concern. Overall, the environmental impacts associated with W&Ds on local neighborhoods are significant in terms of magnitude and geographic scale, particularly given the massive expansion of the warehousing industry. However, W&Ds have still not got equal attention as traditional LULUs, although environmental and planning agencies including California Air Resources Board have considered including warehouses into emission inventories of stationary sources (Smith et al. 2001; Regional Air Quality Task Force 2005).

Warehouses are a special type of urban transportation facilities. Studies on the environmental justice problem in urban transportation have emerged in the past two decades. Disparities are found in the process of transportation decision-making as well as in the distribution of transportation benefits and costs (Schweitzer and Valenzuela 2004). First, not all population groups have the same access to influence the decision-making processes of transportation projects (Lee 1997; Khisty 2000). This discrepancy can lead to an unbalanced distribution of transportation benefit and costs. Second, not all groups of people enjoy equal benefits from transportation investments and regulations. For instance, Bullard (1997) questioned whether all groups of people have equal opportunities for employment in transportation projects. Third, transportation facilities and operations can be locally undesirable as they generate considerable negative externalities, and disadvantaged communities are found disproportionately affected by these transportation costs (Bullard 1997; Gwynn and Thurston 2001). Warehousing establishments create tax revenues and job opportunities, which are particularly welcomed by many cities with high unemployment rates (Husing 2010). However, those benefits are comparatively limited and will be even more so in the near future. First, the warehousing industry generally has lower job densities than many other industries (Dablanc 2014). As warehousing systems are getting more sophisticated and automated, the demand for jobs, especially low-skilled jobs, will be decreasing. The job creation benefits may not last long unless the education and skill levels are improved significantly (Husing 2016). Such an improvement is nonetheless not easy to achieve in the short term. Second, warehousing jobs are not well-paid or secure in the long run. According to a study on logistics jobs in the Inland Empire, the California Budget Project estimated that in this region, workers required an hourly wage of US$17.48 to meet basic family needs, but only 3 percent of workers in blue-collar warehouse occupations could reach that threshold (Bonacich and De Lara 2009). The protection of low-income workers from the warehousing firms is weak, especially during economic crises. Many of warehousing jobs are temporary, furthering undermining the security and stability of employment benefits that warehousing establishments bring to local neighborhoods (Bonacich and De Lara 2009; De Lara 2009). Third, the low per acre tax revenues and absence of sales taxes associated with warehousing development are other concerns of local governments (Dablanc 2014). Therefore, the benefits from warehousing development are dwarfed by the externalities following the development. Compared to other transportation facilities like airports and rail yards, W&Ds are entirely private projects and more footloose, and their location choice is more subject to localized policies. The cost–benefit analysis of policies on the warehousing industry is worth more attention not only from the governments’ side but also from the perspective of local inhabitants.

Framing the Problem: Understanding the Progress of Environmental Justice Research

A detailed review of the progress on environmental justice can help planners better conceptualize and measure the disparities in warehousing location. The problem of environmental justice was initially raised in the 1980s, and the first wave of studies (Bullard 1983; US General Accounting Office 1983; United Church of Christ [UCC] 1987) focused on the disproportionate distribution of hazardous waste landfills in disadvantaged neighborhoods, especially in black neighborhoods. Environmental justice attracted increasing attention in the recent decades and becomes one of the most popular topics in the fields of planning and public policy. The concept of environmental justice emphasizes the disempowerment of people to avoid disproportionate environmental burdens and involves stakeholders including various levels of governments, public agencies, and private entities (e.g., UCC 1987; Bullard 1996; US EPA n.d.).

Researchers have identified three explanations as the causes of environmental injustice: economic, sociopolitical, and racial (Mohai and Saha 2007; Mohai, Pellow, and Roberts 2009). First, LULUs require places with low land rents and unskilled labor, and these places are likely to be where poor people (or minority people under certain circumstances) live. The economic explanation also implicitly includes the market dynamics following the siting of LULUs: due to the externalities associated with these land uses, the neighborhoods become less environmentally desirable and thus reduced housing rents attract more disadvantaged people (e.g., Mohai and Bryant 1992). Second, LULUs intentionally choose locations with the least effective political resistance. In many cases, neighborhoods dominated by disadvantaged residents are less likely to have the necessary political resources and power to fight against the siting of LULUs (e.g., Hamilton 1993; Bullard 1996). Third, racial discrimination in various institutional and social processes contributes to environmental injustice as well. Zoning regulations, public policies, land use decisions, and housing dynamics can be biased against disadvantaged populations (Silver 1997; Cole and Foster 2001). Comparably, the economic explanation is more related to socioeconomic status, the racial explanation focuses on the ethnic background, while the sociopolitical explanation concerns about both factors. The three explanations are often mixed together and function simultaneously in the interactions between LULUs and local communities.

The scope of environmental justice studies has been expanding. Why did early studies almost exclusively focus on toxic waste landfills? One reason is the apparent environmental hazards generated by these facilities, while another lies in the limited availability of data. Dating back to 1980s, only certain types of facilities were recorded and monitored by public agencies. As Bullard (1996) later pointed out, environmental injustice exists in the spatial distribution of many other land uses. For instance, air quality has been a popular topic during the last decades, and various studies examined different types of polluting sources including fixed ones like heavy manufacturers and mobile ones such as heavy-duty vehicles (e.g., Szasz and Meuser 1997; Gwynn and Thurston 2001; Pastor, Sadd, and Morello-Frosch 2004; Houston et al. 2004). These studies cover not only the spatial distribution of the polluting sources but also the diffusion of pollutants as well as the exposure of population groups to the air pollutants. Technological advances in precise measurement and monitoring of air pollutants firmly support the research progress in this area. Urban green space, climate changes, transportation mobility, and flood threats are among the many research subjects of latest articles (e.g., Adger, Paavola, and Huq 2006; Maantay and Maroko 2009; Walker 2012; Wolch, Byrne, and Newell 2014). There are a number of studies touching the environmental justice problems caused by freight activities, while most of the efforts concentrate on sea ports or freight corridors, especially on the health threats of emissions from freight trucks (Giuliano and O’Brian 2007; Wu et al. 2009; Kozawa, Fruin, and Winer 2009). Warehousing facilities and activities have nevertheless not received adequate attention so far.

A detailed review of empirical studies on environmental justice highlights several enduring debates related to methodology and data. The first one is about the temporal dimension of research design. In the first generation of environment justice research, cross-sectional data analysis was dominant. The disproportionate distribution of environmental hazards in disadvantaged neighborhoods at a certain point of time was regarded as evidence of environmental injustice. As theoretical discussions continued, empirical research had stepped into a new era since the classic question of “which came first” was raised in the 1990s (Hamilton 1993; Been 1994). A growing number of studies investigated the time sequence and causal relationship between the siting of LULUs and the move in of the disadvantaged population by analyzing longitudinal data. The longitudinal analysis provides more profound results and implications to understand the mechanisms behind environmental injustice problems, although conclusions are still mixed (see, Mohai and Saha 2015). Pastor, Sadd, and Hipp (2001) introduced the simultaneous equation model into the discussion and shed some light on the interdependent relationship between the location choice of LULUs and the housing location choice of minority people. Therefore, longitudinal data have become more available, and it greatly contributes to a more systematic understanding of environmental justice. But cross-sectional analysis is still of importance, particularly when longitudinal data are not present. Many studies implicitly suggest that the spatial “colocation” of environmental hazard and the disadvantaged population is a strong indicator of environmental injustice (Schweitzer and Valenzuela 2004).

The second debate is about the geographic scope of study and unit for analysis. Taking some longitudinal studies as an example, the choice of the geographic dimension could result in very different conclusions. Nationwide longitudinal studies (e.g., Oakes, Anderton, and Anderson 1996; Been and Gupta 1997; Hunter et al. 2003) rarely provide sufficient support for the existence of environmental injustice. However, region-level studies (e.g., Shaikh and Loomis 1999; Pastor, Sadd, and Hipp 2001; Hipp and Lakon 2010) generally confirm the disparate siting process, although they conclude differently on the postsiting process. The location choice of LULUs is strongly affected by various regional and local factors such as zoning and environmental regulations, so the failure to control such heterogeneity largely undermines the validity of many nationwide studies. Region-level studies are less affected by such weaknesses as the observations are within the same land use and housing markets, and localized effects are easier to control. Meanwhile, the debate on how to measure the spatial relationship between environmental hazards and geographic units has been vigorous. Mohai and Saha (2007, 2015) reviewed methods used in different empirical studies and indicated that the distance-based methods would be more appropriate than the unit–hazard coincidence methods. The unit–hazard coincidence methods directly link each environmental hazard to the unit containing it, while the distance-based methods consider the spatial range of the environmental impacts and link the hazard to units within a certain distance. The disparities in the distribution of hazards could be underestimated using the unit–hazard method especially when the hazards are located close to the boundaries of units, and such underestimation could be largely avoided using the distance-based methods. However, the distance-based methods are not perfect. The distance thresholds used to measure the environmental disparities are largely arbitrary and could be highly subject to the spatial range of environmental impacts. Finally, a rising number of studies emphasized the importance of the spatial autocorrelation in evaluating environmental justice, and the application of geographic information system techniques greatly helps control the possible biases caused by spatial autocorrelation (e.g., Pastor, Morello-Frosch, and Sadd 2005; Chakraborty, Maantay, and Brender 2011).

Location Choice of Warehouses and Disadvantaged Population

To better understand why warehousing facilities tend to be disproportionately distributed in disadvantaged neighborhoods, I examine two relevant socioeconomic processes: the firm location choice of warehouses and the housing location choice of disadvantaged population. These two processes are not only spatially and temporally correlated but also likely to have a causal relationship with each other.

Studies on the warehousing location choice have been emerging in the recent two decades, and Table 1 summarizes the methodologies and findings in several critical pieces. Traditional factors including transportation accessibility (e.g., Bowen 2008; Verhetsel et al. 2015), labor costs (Sivitanidou 1996; Demirel, Demirel, and Kahraman 2010), and effects of agglomeration (Warffemius 2007) remain significant in the location choice of warehouses. However, various changes in warehousing and supply chain management, as well as urban form and infrastructure, have been reshaping the landscape of warehousing distribution.

Table 1.

Summary of Studies on Location Factors of Warehouses.

Author	Year	Methodology	Variables Involved
Sivitanidou	1996	Multivariate modeling	Freeway mileage, distance to airport, size of facility, distance to large blue-collar worker pools, age of building, distance to consumer markets, distance to areas of manufacturing employment, etc.
Korpela and Tuominen	1996	Analytic hierarchy process	Compliance, capacity, cooperation, urgent deliveries, transportation, facilities/equipment, skills of personnel, etc.
Korpela, Lehmusvaara, and Nisonen	2007	Analytic hierarchy process and data envelopment analysis	Delivery time, quality and quantity of deliveries, urgent deliveries, frequency, special requests, capacity, etc.
Warffemius	2007	Interview	Economies of agglomeration, traffic congestion, etc.
Bowen	2008	Statistical comparative analysis	Proximity to infrastructure (highway, airport, rail, and marine infrastructure).
Demirel, Demirel, and Kahraman	2010	Multicriteria Choquet integral	Transportation costs, labor costs, availability of labor force, existence of modes of transportation, proximity to customers/suppliers, zoning and construction plan, etc.
Durmuş and Turk	2014	Logistic regression modeling	Distance to transportation nodes, distance to CBD, densities of firms in industry and service sectors, rents.

Note: CBD = central business district.

First, as stated above, modern warehousing facilities require a huge amount of space for storing and processing goods and accommodating automatic warehousing systems. Furthermore, developers usually avoid multistory buildings, given the limited revenues warehousing firms produce per unit area. Therefore, the dimensions of warehousing buildings including width and story height increase significantly, and developers demand land parcels with large sizes and certain shapes. While the land rents in the central areas substantially go up, warehousing firms have been leaving for suburbs with cheap land and large parcels. Second, fully fledged urban transport network provides warehousing firms better accessibility. Cities, especially gateway cities, have spent a lot on transport infrastructure including freeways, airports, seaports, and intermodal facilities. The improved transport access throughout the metropolitan areas offers suburbs great conveniences. Urban cores no longer have dominant advantages over the periphery, especially when considering the aggravated congestion in the city centers (Giuliano 2004). Transport costs become increasingly even across places in the same metropolitan areas, and such changes offer warehousing firms more flexibility in location choices. Third, warehousing developers seek for low-wage (normally low education attainment) labor force. Cities with cheap housing and many unskilled minority immigrants become particularly attractive (De Lara 2009, 2013). It nonetheless causes the concern over the racialization of logistics labor market (Bonacich, Alimahomed, and Wilson 2008). For instance, in the Inland Empire, blue-collar warehousing jobs are much more closely linked to minority residents, especially the Latinos (Husing 2006). Fourth, according to McKinnon (2009), W&Ds cluster more to benefit from agglomeration economies through sharing infrastructure and maintenance support. Fifth, the spatial organization of warehousing facilities is changing in response to the market and supply chain fragmentation (Rodrigue 2008). Customers increasingly rely on global production and distribution; therefore, warehouses are expected to handle shipments from and to widespread locations. Meanwhile, geographically fragmented supply chains require warehouses to deliver goods at low costs while with higher time reliability (Hesse and Rodrigue 2004). As a result, warehouse developers make location decisions based on demands from regional markets and resources to make efficient and timely deliveries. The “new spatial logic” no longer only focuses on local and intrametro demands but more on regional, national, or even international connections (Hesse 2007).

Finally, the roles of social and institutional factors become increasingly significant in the location choice of warehouses. The interactions between logistics firms, real estate agents, and municipalities have strongly shaped the “spatial imaginary” of logistics network (Cidell 2011). Local planning authorities develop their lists of desirable industry types based on the cities’ financial status, development stage, land use compatibility, and the policies of neighboring municipalities. Zoning restrictions, environmental regulations, and economic incentives are common policy tools that local governments use to encourage or discourage particular kinds of land uses (Dablanc and Rakotonarivo 2010; Cidell 2011; Christensen Associates, Grow & Bruening, and Kathryn H.S. Pett 2012). Industrial realtors, as a mediator, work with both municipalities and logistics tenants to match the supply demand of properties. Dablanc (2014) studied two cities in the Los Angeles region and found that employment opportunities and tax revenues are the major factors that local governments consider in making industrial policies. The study also implied that the attitudes of local municipalities toward logistics land uses also largely depend on the financial performance of the governments themselves. Apart from these studies, how public policies influence warehousing location has not been examined and explained in depth with empirical evidence. On the other hand, the political opinions and actions of local stakeholders on the siting of W&Ds become increasingly influential, but research on this topic is also largely absent. All the changes in warehousing location choices contribute to the “logistics sprawl,” which has been found in various metropolises around the world (Dablanc and Ross 2012; Dablanc, Ogilvie, and Goodchild 2014). Warehousing developers increasingly tend to site their facilities in the suburban neighborhoods with (1) cheap land and large parcels, (2) ready transport access, (3) good regional connections, (4) low-wage labor, and (5) favorable sociopolitical environment. What are the implications of this tendency on the issue of environmental justice? Neighborhoods with low land rents, labor costs but without effective political resistance are always where socially deprived population concentrate. The changing factors in the location choice of W&Ds therefore contribute to the spatial expansion of environmental disparities.

The theory on the housing choice of the disadvantaged population is developed upon the literature on residential location choice, but it, in particular, emphasizes the constraints and disadvantages of these people in choosing where to live. Among the determining factors of the residential location choice, local public goods provision, commuting costs, housing rents, quality of built environment, socioeconomic attributes of neighborhoods, as well as household and personal preferences have all been extensively studied (e.g., Weisbrod, Lerman, and Ben-Akiva 1980; Bayoh, Irwin, and Haab 2006; Schirmer, van Eggermond, and Axhausen 2014). People consider these factors, make trade-offs, and pick the options that maximize their utilities. Due to various limits, socially deprived population nevertheless have fewer choices. The literature on residential segregation may further help understand how these people are constrained in the housing market. Existing research ascribes residential segregation to three factors: socioeconomic disparities, housing market discrimination, and race-based residential preferences (e.g., Clark 1986; Bobo and Zubrinsky 1996; Jarvis 2015). Differences in socioeconomic status and purchasing power make poor or minority people less competitive in bidding for housing options with high levels of amenities. Consequently, the immobility of amenities, disparities in housing budgets, and competitive housing market jointly lead to residential segregation. Meanwhile, public policies, regulations, and housing market behaviors could also make it difficult for minority people to integrate into majority neighborhoods. The housing market discrimination against the minorities is particularly evident when people of different races have similar purchasing power and housing budgets. Despite the argument that residential preferences vary among races, the spatial distribution of different population groups is generally more of competition driven than preference driven. These factors in explaining residential segregation are highly correlated with those causing the environmental justice problem in warehousing location. Thanks to these factors, disadvantaged population are more likely to end up living in neighborhoods with warehousing facilities and subsequent negative externalities.

Conclusion

The environmental justice problem in warehousing location is still a new topic with very limited theoretical thinking and empirical evidence. This article focuses on the disproportionate distribution of warehousing facilities in disadvantaged neighborhoods and explains how the disparities result from the interactions of various socioeconomic processes. This article reviews relevant literature on the externalities of warehousing activities, the recent progress of environmental justice research, and the location choices of W&Ds and disadvantaged population. From the perspective of environmental justice, the environmental hazards associated with warehousing activities affect the spatial relationship between warehouses and communities. The factors in the firm location choice of warehousing facilities and housing location choice of disadvantaged people jointly lead to the spatial imbalance of warehousing distribution. Those people have to suffer from more environmental burdens, given their disadvantages in competition over housing. Comparably, warehousing developers have nonetheless much more choices, especially when some cities favor warehousing industry, and the resistance from local residents is less effective.

To mitigate the disproportionate environmental burdens of warehousing facilities, policy makers might consider the following options. First, it is necessary to enhance the accuracy and transparency of environmental impact reporting in planned warehousing projects. Meanwhile, to raise the awareness of the environmental impacts among local residents could equally reduce any information asymmetry. The potential environmental hazards of warehousing projects and the proposed measures developers would do to mitigate the hazards should be well conveyed to people who live nearby. Second, local residents, especially those affected by warehousing externalities, should be empowered, so that they can exert more effective influence over land development decisions. The city council of Moreno Valley (in Riverside County, California), for instance, was sharply criticized by environmentalists for adopting the initiatives submitted by the developer of the World Logistics Center rather than allowing a vote by local residents (Beaulac 2016). People living in neighborhoods with extensive warehousing development could turn to environmental and land use laws in case the representatives fail to effectively protect their living space. Third, governments may consider improving the environmental standards and regulations in the construction of warehousing facilities and thus mitigate the environmental burdens on local communities. Promoting more effective use of land in warehousing development by encouraging innovations in architecture and landscape design could also alleviate the possible damage to the built environment. For example, the city of Pico Rivera (in Los Angeles County, California), which had long been a hot spot of warehousing development, began to adopt more stringent regulations on the design and buffer of warehousing uses. These regulations appear to be rather helpful for mitigating negative impacts of those land uses. Finally, economic and administrative approaches may help address the environmental justice problem as well. Policy makers could develop schemes that internalize warehousing-related externalities and compensate affected disadvantaged people accordingly. Such approaches have not been in effect yet, but similar cases are readily available in environmental planning.

After all, the discussion on the environmental justice problem in warehousing location will continue and grow. In particular, the literature has not answered how local governments contribute to the environmental inequity through policy-making and planning practices. Given the central role that local authorities play in the land use decision-making, their attitudes and preferences can largely encourage or hinder warehousing development and further affect the spatial distribution of warehousing-related environmental externalities. A careful examination of the relationship between local institutional factors and established spatial inequality of warehousing location can help policy makers better understand the planning problem and produce effective solutions to address it.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

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