Saturday, January 22, 2022

Valuing the Impact of Air Pollution in Urban Residence Using Hedonic Pricing and Geospatial Analysis, Evidence From Quito, Ecuador

This study attempts to determine the marginal willingness to pay for cleaner air in the Metropolitan District of Quito (DMQ) Equador by estimating the impact of air pollutants on property values. Spatial interpolation techniques portray pollutant concentrations in the DMQ.  A hedonic model estimates air pollution impacts on properties.  Impacts of three pollutants, (Particulate Matter-PM2.5, Nitrogen Dioxide-NO2, and Sulfur Dioxide-SO2) were estimated. The impact were statistically significant with decreases in property values of between 1.1% and 2.8%, or between $1,846.20 and $4,984.74 US$.
The most significant impact was from NO2 with a coefficient of ‒2.765, meaning that an increase in 1% of this pollutant will have a 2.8% decrease in the property price.  NOx is one of the very few air pollutants that people are able to perceive. This result is significant since the average residence value is 865.13 US$/m2, meaning a reduction in house value of 23.92 US$/m2. The other one is O3, which was also statistically significant, but only at 10%. The impact of a 1% increase in concentration of O3 will decrease the residence value 7.41 US$/m2. The other two air pollutants are odorless. PM2.5, had a coefficient of ‒1.733 and was statistically significant at the 95%  level. implying that an increase of 1% in PM2,5 concentrations will reduce a property's value 14.99 US$/m2. CO had a coefficient of ‒1,103 and was significant at 99%, meaning that an increase in CO concentration will reduce home value by 9.54 US$/m2
by Sebastian Borja-Urbano, Fabián Rodríguez-Espinosa, Marco Luna-Ludeña
Universidad de las Fuerzas Armadas – ESPE, Sangolquí, Ecuador
Corresponding Author: Fabián Rodríguez-Espinosa, Departamento de Ciencias de la Tierra y Construcción, Universidad de las Fuerzas Armadas ESPE, Av. Gral. Rumiñahui s/n, Sangolquí, Ecuador. Email:
Air, Soil and Water Research via Sage Publications
Volume 14; First Published November 22, 2021; Open Access

Economic Evaluation of the Indoor Environmental Quality of Buildings: The Noise Pollution Effects on Housing Prices in the City of Bari (Italy)

Among environmental factors, noise represents one of the most relevant determinants on human health and on the urban quality level and, consequently, on real estate values. Thus, the noise pollution issue plays a significant role in public urban policies aimed at increasing the acoustic comfort level and creating more sustainable and comfortable cities. The real estate market  is highly sensitive to noise factor and the residential prices can be strongly influenced by a high acoustic pollution rate. The present research aims to analyze the functional relationships between noise pollution and selling prices in four municipal areas of the city of Bari (Southern Italy). For each area, a study sample constituted by two hundred residential properties sold in 2017–2019 was detected for the identification of the main influential factors on prices and the investigation of the contribution of noise on them. The implementation of an econometric technique was used to obtain four different models (one for each municipal area of the city of Bari) able to explain the specific impact of noise pollution level on selling prices. From the comparison of the results obtained for each area, the outputs confirm the expected phenomena in terms of a decrease of noise component influence on residential prices from the central area to the peripheral. For the suburban area of the city of Bari, the model obtained does not include the noise pollution factor, showing a lower (scarce) importance of the environmental factor among the buyer and seller bargaining phases.

..The sound level (Ld) is expressed in decibels dB(A), measured on day, evening and night intervals, in the street where the residential unit is located. The data is derived from  the Strategic Noise Map of the Bari agglomeration, published in June 2017 by the Scientific Directorate of the Regional Agency for Environmental Prevention and Protection of the Puglia Region (ARPA Puglia) with (i) Rating 1: <40 dB(A); (ii) Rating 2: >40 dB(A) and <50 dB(A); (iii) Rating 3: >50 dB(A) and <55 dB(A); (iv) Rating 4: >55 dB(A) and <60 dB(A); (v) Rating 5: >60 dB(A) and <65 dB(A); (vi) Rating 6: >65 dB(A) and <70 dB(A); (vii) Rating 7: >70 dB(A) and <75 dB(A).
The average percentage decrease in the selling price corresponding to a variation from one Ld level to the next one is almost constant for the OMI central area (-3.31%) and for the OMI semi-central area (-3.46%) of the city of Bari. Conversely, for the peripheral area, the average percentage decrease in the housing price corresponding to a passage from a Ld level to the next one is lower and equal to -2.47%. This confirms the lower influence given by the factor related to the noise pollution on selling prices in the municipal OMI area in which the pollution is minor, due to less levels of road traffic and industrial traffic and less of a preference for recreational activities.

For the suburban municipal area, the model does not include the variable Ld among the influencing factors on selling prices. Thus, it is evident that a lower noise pollution level (average sound level Ld for the central area is equal to 64.8 dB(A), whereas the sound level Ld for the suburban area is equal to 57.02 dB(A)) corresponds to a less significant effect of the acoustic factor in the selling price formation.

by Pierluigi Morano 1 , Francesco Tajani 2 , Felicia Di Liddo 1,* and Michele Darò 3
Buildings  Volume 11, 213; Published: 19 May 2021
open access 
1 Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh),
Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy;
2 Department of Architecture and Design, Sapienza University of Rome, Via Flaminia 359, 00196 Rome, Italy;
3 Mi.Da—Sound Design, Via Porro 9, 10064 Pinerolo, Italy;

Wednesday, January 19, 2022

Comparing Pollution Where You Live and Play: A Hedonic Analysis of Enterococcus in the Long Island Sound

Hedonic property value studies of water quality conventionally focus on water quality levels measured nearest a home. This study examines whether water quality at the nearest access point (i.e., a beach) matters more to local residents.  Megan Kung, Dennis Guignet and Patrick Walsh conduct a hedonic analysis of water quality in the Long Island Sound, where an aging infrastructure and heavy precipitation lead to frequent sewage overflows. The analysis focuses on bacteria contamination and beach closures at various access points and monitoring sites. Results suggest that decreases in water quality measured at the nearest beach yield a larger negative effect and impact homes at a farther spatial extent than previously suggested in the literature.
Model 1 has two variants (SAC and FE) which follow the conventional approach in the literature and link homes to the water quality measures at the closest monitoring site.  The SAC is a general spatial model, which includes a spatiotemporal lag of neighboring house prices as a means to account for spatially correlated omitted variables and FE is a municipality-by-year fixed-effects model.  The SAC 1 results suggest homes within 500 meters (m.) of the Sound are affected the most, experiencing an average decrease in price of 0.16% for a 10% increase in enterococci. For the homes in this distance bin, which have an average price of $1,001,651, this translates to an average decrease in home value of $1,583. A similar elasticity is estimated for the 500–1,000 m bin. Any negative elasticity estimates beyond 1,000 m are statistically insignificant. The estimated price effects are small relative to the overall price of a home, but are in line with previous estimates.  The corresponding FE 1 model suggests a negative, but small and statistically insignificant, elasticity in the nearest-distance bin. The –0.0127 elasticity in the 500–1,000 m bin is similar to that of the SAC 1 model, but the two models differ in that the FE 1 model suggests possible negative elasticities as far as 2,000 m.
Cladophora, a wiry green seaweed, is found in great abundance in Little Narragansett Bay, fertilized by a high load of nitrogen entering the bay. Dense mats of the seaweed are oxygen factories during the day, but use up all of the oxygen during the night, leaving none for the animals. Only animals tolerant of very low oxygen, ones who can essentially hold their breath through the night, are found in areas where this seaweed is thick. In mid-summer, the seaweed is so abundant and productive, excess oxygen bubbles out of the water and can cause large mats of the seaweed to float to the surface. Credit: Jamie Vaudrey, UConn

In SAC and FE Models 2 and 3, the authors deviate from the conventional approach of matching to the nearest monitoring site, and instead explicitly account for water quality at the nearest beach. In doing so, they see that any previously negative coefficients corresponding to water quality at the portion of the waterbody nearest the home are now statistically insignificant.

In Model 2 when they consider water quality at the closest beach (conditional on water quality measured nearest the home), there is a strong negative effect that is larger in magnitude and spatial extent. SAC 2 shows that among homes nearest a beach (0–500 m bin), a 10% increase in enterococci decreases house prices by 0.31%. These homes have an average price of $1,259,349, and so this translates to an average implicit price of $3,904. This result is virtually identical in the FE variant of Model 2.  Results suggest that the negative elasticity associated with water quality at the nearest beach could extend to 3,500 m. For homes in the farthest significant distance bin in SAC 2 (3,000–3,500 m, which have an average price of $844,851), the mean implicit price for a 10% increase in enterococci levels at the nearest beach is a decrease of $1,369.  The FE 2 model suggests that this effect is even larger; the estimated –0.0531 elasticity for the 3,000–3,500 m bin suggests an average implicit price of $4,486. The statistically significant effects of beach enterococci levels are not consistently found in all distance bins out to 3,500 m.
In a meta-analysis of hedonic property value studies examining water quality, Guignet et al. (2020) report a mean elasticity with respect to fecal coliform counts of –0.018 and –0.020 for waterfront and non-waterfront homes within 500 m, respectively. These are quite similar to our estimated elasticities with respect to enterococci counts.
In the SAC 3 and FE 3 models, they account for the number of beach closure days in summer. The results suggest that home buyers and sellers do, on average, respond more to beach closures than to enterococci levels. The estimated price effects of beach closures are of the expected negative sign, with robust and statistically significant negative effects in all distance bins out to 2,000 m from the beach. With the exception of the 2,000–2,500 m bin, we find significant negative effects extending out to 3,500 and even 4,500min models SAC 3 and FE 3, respectively. In general, the estimated beach closures effect in the SAC and FE models are very similar. These more robust, farther extending, and statistically significant estimates seem reasonable given that beach closures and notifications are a more direct and salient signal to local residents regarding water quality. When comparing estimates across the variants of Models 2 and 3 in table 3, we see that accounting explicitly for beach closures decreases the magnitude and/or significance of the estimated beach enterococci elasticities, especially in the nearest-distance bins.
For one additional beach day closed each summer season, the estimates translate to an average decrease in home value of $2,123 for homes in the 0–500 m bin, and $598 for homes in the 3,000–3,500 m bin. These estimates suggest that if the nearest beach is closed an additional week every year, there would be an average price decrease of $14,859 for homes in the 0–500 m bin, and $4,188 for homes in the 3,000–3,500 m bin. This is a plausible magnitud  given that the average number of beach days closed per season is seven, and there have been instances where beaches were closed for most of, or even the entire, season.
With these caveats in mind, local stakeholders can still make better-informed decisions by comparing the potential house price effects estimated in this study with the costs of policies and projects to improve water quality in the Long Island Sound.... Consider a hypothetical program in New Rochelle, a city in Westchester County, that reduces the number of beach closures each summer season from the average of seven days a year to zero. Our results from SAC 3, for example, suggest that this would yield a total increase in value of the 5,672 single-family homes and townhomes within 3.5 km of a beach in New Rochelle by about $50.2 million. Note that we omitted homes in the statistically insignificant 2,000–2,500m. bin for this calculation. As a rough comparison, a project to repair the sewer infrastructure and prevent stormwater infiltration and subsequent sewage overflows in New Rochelle cost about $20 million (Garcia 2015b), which is substantially less than the estimated capitalization effects in this purely illustrative example. These capitalization effects reflect only a portion of the benefits to local stakeholders because households farther away who use these beaches will also benefit.
by Megan Kung 1, Dennis Guignet 2, and Patrick Walsh 3
1. Economist, Los Angeles Regional Water Quality Control Board, 320 W. 4th Street #200, Los Angeles, CA 90013 USA (email: 
2. Assistant Professor, Department of Economics, Appalachian State University, 416 Howard Street, Room 3101B, Peacock Hall, ASU Box 32051, Boone, NC 28608 USA (email:
3. Economist, National Center for Environmental Economics, US Environmental Protection Agency, 1200 Pennsylvania Avenue, NW, Washington, DC 20460 USA (email:
Published online December 3, 2021.
Marine Resource Economics via The University of Chicago Press on behalf of the MRE Foundation Volume 37, Number 1, January 2022.

Tuesday, January 18, 2022

An Analysis of US Subsidies for Electric Buses and Freight Trucks

... Congress may create entirely new subsidies for commercial electric vehicles and associated charging infrastructure included in both the Clean Energy for America Act and the Build Back Better Act. This issue brief analyzes the carbon dioxide (CO2) reductions and fiscal costs of subsidies for transit buses and certain trucks.

Medium- and heavy-duty vehicles (that is, anything larger than a passenger vehicle) consume roughly 30 percent of the total energy used by on-road or “highway” vehicles and generate about one-quarter of GHG emissions from the transportation sector (equivalent to 7 percent of total US emissions). 

... The fiscal costs and GHG reductions of the electric truck subsidies will depend on how much truck buyers respond to the subsidies and how much those trucks are driven, but because these subsidies are brand new, it is that much harder to anticipate their effects and assess how much the subsidies may help achieve the Biden administration’s climate objectives.

Joshua Linn and Wesley Look study the potential effects of offering tax credits to transit buses, day cabs (freight trucks that do not include a sleeping compartment), and sleeper cabs (freight trucks that include a sleeping compartment). The three vehicle categories account for almost half of carbon dioxide (CO2) emissions from medium and heavy-duty vehicles (MHDVs), with sleeper cabs making the largest contribution of the three types. 

They analyze a subset of the vehicle types that are eligible for subsidies, and are not estimating the total effect of the policies on all MHDVs.

They use a new computational model of MHDVs that accounts for the effects of subsidizing 30 percent of the up-front purchase cost of transit buses, day cabs, and sleeper cabs to estimate the uptake, fiscal costs, and CO2 benefits of the subsidies through 2035, relative to a baseline case that does not include the subsidies. They consider scenarios that differ by the rate at which electric vehicle prices decline over time; in all scenarios, the subsidy phases out after electric vehicles achieve 50 percent market share.

Their key findings are:
1. In the baseline case (no subsidies), electric buses, day cabs, and sleeper cabs are unlikely to achieve significant shares of new purchases by 2035.
2. The effectiveness of the 30 percent subsidy at increasing electric bus and truck sales depends on the assumed rate at which electric vehicle prices decline. Assuming a moderate rate of pre-subsidy price decline, the subsidy causes electric bus, day cab, and sleeper cab sales to begin increasing around 2030 and achieve a 50 percent market share in 2035.
3. Assuming a faster rate of price decline, the subsidy causes electric buses, day cabs, and sleeper cabs to achieve a combined 80 percent market share by 2035. At a faster rate of price decline, the subsidy reduces emissions by about 60 million metric tons of CO2 in 2035, which amounts to about a 60 percent decrease in emissions relative to the baseline (no subsidy) scenario.

Figure 1. Total Sales (number of new EV trucks sold per year

Total (undiscounted) fiscal costs between 2022 and 2031 are $2-24 billion, depending on the degree of price decline, with faster decline causing greater uptake and higher fiscal cost. Fiscal costs per ton of CO2 reduction are broadly comparable to recent estimates for subsidizing plug-in electric light-duty vehicles.

Note that the Biden administration’s target is for total US GHG emissions in 2030 to equal half of total emissions from 2005. The emissions reduction in 2030 for transit buses, day cabs, and sleeper cabs in the high-technology scenario amounts to 1.4 percent of the total emissions reduction needed to achieve that target. 
An individual buyer considering either a bus or cab trades off up-front purchase costs against fuel and maintenance costs. For example, they assume that an all-electric bus has a purchase price of about $185,000 in 2030 (not including subsidies), which is almost 50 percent higher than the price of a diesel bus.
Figure 3. Percent of Electric Buses and Trucks on the Road

Monday, January 17, 2022

Systematic Variation in Waste Site Effects on Residential Property Values: A Meta-Regression Analysis and Benefit Transfer

This article presents a meta-analysis based on 727 estimates from 83 hedonic pricing studies to provide new insights on the effects of waste sites on residential property values. Relative to previous meta-analyses on this subject, estimates are corrected for publication bias and the ability of the meta-regression model to produce reliable benefit-transfer estimates is assessed. Proximity to severely contaminated waste sites has a supremely negative impact on residential property values, whereas on average the distance from non-hazardous waste sites has no effect. Correcting for publication bias has a sizeable impact, reducing the average effect size by up to 38%. Benefit-transfer errors based on the meta-regression model are fairly large and, in line with the broader literature, outperform simple value transfer when the underlying data sample is heterogeneous. 
The corrected average effect size translates into a 1.5% to 2.9% property value increase per mile of increased distance from a waste site for a house at a one-mile distance. These estimates are situated in the lower range of values produced by the previous literature. The results are generally robust across justifiable estimators, weighting schemes and the replacement of moderators.
The subsample analyses revealed distinct differences for severely contaminated sites on the NPL and non-hazardous waste sites. As non-hazardous waste sites do not reduce property values on average, they are not considered a disamenity in these average cases. By contrast, severely contaminated waste sites on the NPL clearly reduce residential property values on average, with an estimated mean effect size of 42.2%. 

by Marvin Schütt; Institute for Environmental, Resource and Spatial Economics, Kiel University, Wilhelm-Selig-Platz 1, 24118, Kiel, Germany
Environmental and Resource Economics via Springer
Volume 78, 2021; Pages 381–416; Published: 24 February 2021

Sunday, January 16, 2022

CAFOs and Surface Water Quality: Evidence from Wisconsin

Concentrated animal feeding operations (CAFOs)—animal feeding operations with over 1,000 animal units in confined spaces—have proliferated over the past thirty years in the United States. CAFOs provide operational cost savings, but higher animal concentrations in confined spaces can generate external costs, for example, non-point source water pollution. In this study,  Zach Raff  and Andrew Meyer improve on previous research designs to estimate the relationship between the growth in CAFOs and surface water quality using longitudinal data on a large spatial scale.  Raff and Meyer use a panel dataset from 1995–2017 that links CAFO intensity with nearby surface water quality readings in Wisconsin to perform our analysis. Leveraging variation in CAFO intensity within hydrological regions over time, they find that increasing CAFO intensity increases the levels of nutrients, specifically total phosphorus and ammonia, in surface water; adding one CAFO to a Hydrologic Unit Code-8 (HUC8) region leads to a 1.7% increase in total phosphorus levels and a 2.7% increase in ammonia levels, relative to sample mean levels. As an important contribution of our work, they use these results to calculate the external costs of surface water quality damages from CAFOs in Wisconsin. Our results imply that the marginal CAFO in Wisconsin produces non-market surface water quality damages of at least $203,541 per year.
Raff and Meyer ... use water quality index and benefit transfer methodologies to convert increased nutrient concentrations associated with CAFO expansion to losses in non-market surface water quality benefits, that is, damages. This methodology estimates changes in a water quality index and monetizes the changes with a benefit transfer function. They estimate an annualized WTP (Willingness-To-Pay) of $3–$12 per Wisconsin household for the improved water quality that would exist in the counterfactual world with one fewer CAFO in each HUC8 region. Aggregated to the entire state, Wisconsin households would be willing to pay between $6.9 million and $27.9 million annually for one fewer CAFO in each HUC8 region.
Raff and Meyer are aware of only two monetized estimates of CAFO related external costs in dimensions other than surface water quality. First, Herriges, Secchi, and Babcock (2005) estimate that, in Iowa, upwind CAFOs decrease home prices by 3%, which is roughly $2,500 for the average home in their sample. Second, Sneeringer (2010) estimates that the annual air pollution related damages from hogs in the United States are $31 per animal. Applying this value, a CAFO with 2,500 hogs (equivalent to 1,000 animal units) produces $77,500 in air pollution damages per year. In addition to these monetized damages, previous studies find that CAFO exposure is associated with damages to individual health (Sigurdarson and Kline 2005; Radon et al. 2007)50 and occupational safety (Ramos, Fuentes, and Carvajal-Suarez 2018). Other potential, but not yet studied, damages include commercial fishing impacts, increased contamination of private wells, and increased water treatment costs (EPA 2002b).51 To achieve efficiency in CAFO product markets, policymakers and regulatory agencies can use our results, combined with those of previous studies, to develop policy that factors the external costs of CAFOs into their production decisions.

Also important for CAFO policy, they present evidence that the extreme concentration of manure in fewer locations likely drives the surface water quality damages. Thus, CAFO control policy should require better manure management, for example, subsidies to transport the manure off site, to better control the surface water damages from these operations. However, future work should examine the specific avenue by which nutrients enter surface waterbodies, for example, leaching, spreading....
by Zach Raff 1 and Andrew Meyer 2
1. University of Wisconsin-Stout, Menomonie, Wisconsin, USA.
2. Marquette University, Milwaukee, Wisconsin, USA.
American Journal of Agricultural Economics via Wiley Online American Journal of Agricultural Economics - Wiley Online Library