Defining Urban Waste City Challenge Week 2014 Group Zeta ­ August 27, 2014 Radu Stancut, Yuzheng Zhuang, Arnnop Hualchareonthon, Giovanni Paci, Renate Pinggera

“The problem is that trash doesn’t disappear.”

­ Slajov Zizek (http://the­value­of­garbage.tumblr.com/post/9340763360/slavoj­zizek­in­examined­life)

Table of Contents

Defining Urban Waste

Table of Contents Challenge & Project Scope

Questions to be Answered Project Scope and Constraints

Data and Methods Methodologies of How to Quantify Waste Data Sources, Cleaning and Integration

NYC Department of Sanitation New York Open Data

Limitations Analysis and Results

Development of Waste Quantities Over Time Waste Distribution Per Borough Waste Distribution And Income Waste Costs And Hidden Costs

Global Impact

Definition of Urban Waste ­ group Zeta. August 27, 2014 1

Challenge & Project Scope

Questions to be Answered

Based on the briefing three main questions on urban waste should be tackled: 1. How can we begin to quantify the city's waste in order to make more informed decisions

on waste management? 2. How is New York City waste distributed throughout the city (borough, community district,

per capita etc.)? 3. Using NYC as an example, what are the global impacts of urban waste?

Project Scope and Constraints

The main purpose of this project is to advance a novel methodology for the quantification of urban waste. To the best of our knowledge, getting the numbers right on the amount of waste produced in New York City represents a first important challenge to the policymakers.

Our main contribution is in proposing a way of estimating the amount of waste in the city focusing on the availability of goods that, after consumption, are contributing to waste. To do so, we propose that the City government partners with a number of large private organizations to estimate consumption volumes by product type. Using this information together with accurate survey data on waste volumes and data available from the Department of Sanitation (DSNY) and NYC Open Data, we could develop an accurate measure of the amount of waste produced in the city.

Our methodology is subject to a number of limitations. The most important is that our proposal will rely on the use of proprietary data from several corporations. A second limitation stems from the need to run a government survey.

To complement our work, and also to support the need to invest in our study at community district level, we present a second methodology that seeks to get at the hidden costs of waste management. The idea is to run small randomized experiments at the district level that manipulate the way trash is collected.

Data and Methods

Methodologies of How to Quantify Waste

The quantification of the amount of waste produced in New York City represents a first major obstacle to the establishment of a sound waste policy. Our approach is based on tackling the problem from several different angles. Firstly, not all waste is created equal, so we think of waste as a vector X(g,t)=[x1,x2,...,xN](g,t), i.e. a collection of different possible waste types: e.g. refuse, paper, and metal at curbside “g” at time “t”.

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Secondly, we can think of waste as the “byproduct” of a production (for firms) or consumption (for households) activity. As such the vector “X(g,t)” is generated by a function f(I(g,t);A(g)), where “I(g,t)” is the inputs and “A(g)” are parameters of the function. We propose to estimate a generalized linear model using survey data. “X(g,t)” will be observation measurement for a number of randomly selected curbsides and times in the city, while “A(g)” will include all neighborhood “g” level variables that predict waste such as median income, number of apartment units, and so on. Our main innovation with respect to the extant literature is to look more carefully at the input vector, “I(g,t)”. “I(g,t)” will be a collection of inputs such as: grocery stores in the neighborhoods, mail shipping of commodities (e.g. weight of Amazon shipping, by building), data from food delivery companies such as Fresh Direct or Seamless, Ikea shipments in the city, Best­Buy and Walmart zip code data; and especially pets.com. The idea is to include in this set all possible observables that are inputs in the trash production function. We will then use the estimated coefficients from the survey and the data on “A” and “I” variables at city level to get to a better estimate of the quantity of waste produced, by type. Our methodology has the advantage of giving us parameters on the relative contribution of several observable variables on different components of waste (so we could get predictions by type of waste and look at the relative contribution of several different types of inputs). A second advantage is that the city could partner with these organizations in a more steady manner to obtain a continuous stream of information on waste inputs, and hence adjust the system of collection to temporary variations in local needs.

Data Sources, Cleaning and Integration

The following existing data sources could be identified:

NYC Department of Sanitation The NYC Department of Sanitation (DSNY) offers reports for DSNY Collections in PDF format. These data provide information on residential waste only. The DSNY differentiates between these types of waste collection:

DSNY Curbside Collections: refuse, paper/cardboard recycling, metal/glass/plastic recycling, organic recycling (pilot only)

DSNY Containerized Collections: (same categories as above, but no organic recycling) Other DSNY Collections

Disposed: street basket refuse, street dirt, lot cleaning, other, misc Diverted: organics, lot cleaning: metal bulk, other metal bulk,

Other materials ­ diverted: redeemed bottles and cans, private leaf and yard waste, textile donations, electronics recycling a.o.

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Other materials that are excluded by local law 40 and cannot be counted as “DSNY Managed Waste”: asphalt, tires, dredge material, abandoned automobiles ao.

How is the DSNY data being collected? Curbside and containerized collection routes serve individual districts; DSNY trucks on these routes pass over scales each day which transmit tonnage data into DSNY's centralized computer system. For this reason, monthly statistics, by Community Districts, can be tracked and reported. Usage within this project: The data on “DSNY Curbside Collection” of provides approx. 90% of all residential NYC waste, therefore was considered most relevant. It was extracted manually from PDF files to spreadsheets for visualisation: “Annual Reports for DSNY Curbside Collections (by Borough and Community District)” http://www.nyc.gov/html/nycwasteless/html/resources/reports_ll40.shtml

New York Open Data 1. Data Set “DSNY: Collection Tonnages”

(https://data.cityofnewyork.us/Environment/DSNY­Collection­Tonnages/ewtv­wftx) For each Community District, the tons of Refuse, Paper Recycling and Metal, Glass & Plastic Recycling that were collected by the Department of Sanitation (DSNY) during the calendar month.

2. Data Set “Recycling Diversion and Capture Rates” (https://data.cityofnewyork.us/Environment/Recycling­Diversion­and­Capture­Rates/gaq9­z3hz) for each Community District, its Recycling Diversion rate (percentage of total municipal solid waste collected by the Department of Sanitation (DSNY that is disposed of by recycling) and Capture Rate (% of total Paper or Metal/Glass/Plastic in the waste stream that is disposed of by recycling).

Limitations First, the correlation of amount of waste to density, income and educational level had to be established. Data is available on a community district level and had to be joined to the waste collection data sets. Second, quantified results of programs that have already be launched to reduce waste/increase recycling were hard to find which made interpretation of waste quantification data over time hard and purely based on assumptions. Third, our focus was limited to residential trash, ignoring for the time being commercial waste.

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Analysis and Results

Development of Waste Quantities Over Time The chart shows the tons of waste per day in NYC over the last 4 years. The numbers from 2010 to 2014 show a rather constant amount of waste in the 4 different waste types.

Based on this chart we need to tackle the question why the development is so flat:

1. We need to question the quality of data received from DSNY: Since data is derived from the trucks that collect the trash on a daily basis, perhaps they have a stable number of tracks collecting the trash over the years. For higher amounts of trash they might have external contractors who take care of it and don’t track their tonnage.

2. Economic development: Since 2008/2009 the financial crisis might have affected consumption. Maybe people have been holding back in consuming.

3. Comparison to other city data: We need to look at other American cities like Chicago or Philadelphia to see whether their waste volumes stayed stable, too.

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Waste Distribution Per Borough This chart shows the distribution of the daily tonnage of waste per NYC borough based on the average tonnage of the years 2010 to 2014.

Waste Distribution And Income This chart shows a correlation of yearly household income in $ and the amount of waste based on median income per borough. Except Bronx, according the data from Manhattan, Queens, Brooklyn and Staten Island, there is a linear relationship between Median­household income and tonnage of waste. X­axis is tonnage of waste from 2013, Y­axis is Median­household income. Asterisk: Staten Island Diamond: Manhattan Cross: Queens Triangle: Brooklyn Square: Bronx (outlier)

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Waste Costs And Hidden Costs The measurement of waste costs is complicated by a number of factors. Indeed, the sheer number of dollars spent by a city on refuse collection is but a rough indicator of its cost. Environmental costs, social costs and economic costs need to be taken into consideration. First, the price of collection per unit quantity clearly depends on the market structure. Were a firm to enjoy a spatial monopoly, the price charged would be higher than under competition. Second, a number of economies of scale might potentially be operating: serving a larger territory might decrease the average cost of collection. Other costs of trash collection are likely to be less apparent. For instance, disposal of waste on curbsides might be connected with rats infestations and other diseases. Tourists might find a city less appealing if garbage is abandoned on the side of the road. While a study of the market and costs structures seems too ambitious for our project, we decided to propose a methodology to get at some of the implicit costs. In particular, we would like to focus our attention on the hidden costs associated with trash collection. We will use NYC Open data to get a baseline measure of the health costs associated with waste. We will then propose to work with the city to experiment different ways of trash collection (in buildings with tracks waiting outside, in small locked boxes placed in the streets, and so on). Our outcome variables will be the city data on health in the treated and untreated

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areas as well as small survey and tweets analysis to get at citizen satisfactions of different methodologies of collection.

Global Impact

In the middle of the last century only two cities qualified for mega­city status, defined by a city containing 10 million residents, New York and Tokyo. By the start of the 21st century that number had increased nearly 10­fold and this year (2014) the number of mega­cities surpassed UN expectations set less than a decade ago and reached 28 (http://www.un.org/esa/population/publications/WUP2005/2005WUP_FS7.pdf & http://www.nytimes.com/2014/07/12/business/for­biggest­cities­of­2030­look­toward­the­tropics.html?_r=0). New York has been at or near the top of such lists for this entire stretch of time, displaced first by Tokyo (1975), later surpassed by Mexico City (2000), and more recently by developing, mostly Southern Hemisphere cities (http://www.nytimes.com/2014/07/12/business/for­biggest­cities­of­2030­look­toward­the­tropics.html?_r=0). Current estimates have New York lingering in the top 10 of mega­cities but that will no longer be the case by 2030 at which point 9 of the top 10 are expected to be in either Asia or Africa (http://qz.com/233334/almost­all­of­the­worlds­biggest­cities­will­be­in­asia­and­africa­by­2030/). The growth, both in size and numbers, of mega­cities highlights a still larger global trend toward urbanization. More than half of the world’s population was living in cities by 2010 and projections indicate the global thresholds reaching upwards of 60% in 2030 and 70% in 2050, with the majority of this urban growth taking place in the developing world (http://www.who.int/gho/urban_health/situation_trends/urban_population_growth_text/en/). The concentration of humans across the globe will lead to the generation of increased waste characteristic of urban areas. The waste and trash issues tackled by New York City today will be faced by the majority of humans moving forward. Should such a city as rich as New York in capital, data, city agencies and other resources, be able to raise awareness and develop scalable methods of better handling trash (recycling, waste­to­energy, composting, etc.) the benefits would multiply globally.

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